{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 电商多模态图文检索——Chinese-CLIP baseline全流程示例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
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    "execution": {
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   "source": [
    "<p align=\"center\">\n",
    "    <br>\n",
    "    <img src=\"https://modelscope.cn/api/v1/models/damo/multi-modal_clip-vit-base-patch16_zh/repo?Revision=master&FilePath=resources/Chinese_CLIP_logo_tp_path.svg&View=true\" width=\"300\" />\n",
    "    <br>\n",
    "<p>\n",
    "\n",
    "Chinese-CLIP是OpenAI CLIP模型的中文版本，基于OpenAI CLIP的视觉侧参数，继续使用大规模中文原生图文数据，进行如下图所示的两阶段预训练（\\~2亿中文图文对）。旨在帮助用户快速实现中文领域的图文特征&相似度计算、跨模态检索、零样本图片分类等任务，在[电商多模态图文检索挑战赛](https://tianchi.aliyun.com/competition/entrance/532031/introduction)（即MUGE检索数据集）效果显著。我们这里提供一个基于base规模的baseline全流程实现，方便同学们更简单上手，可以在验证集取得\\~75左右的Mean Recall分数。如果大家想要深入了解和改进模型代码，提高比赛分数，欢迎在Github开源repo了解更多细节\\~\n",
    "\n",
    "<p align=\"center\">\n",
    "    <br>\n",
    "    <img src=\"https://modelscope.cn/api/v1/models/damo/multi-modal_clip-vit-base-patch16_zh/repo?Revision=master&FilePath=resources/chinese_clip_pretrain.png&View=true\" width=\"300\" />\n",
    "    <br>\n",
    "<p>\n",
    "\n",
    "**代码Github开源链接（欢迎star \\& fork🔥🔥）:** https://github.com/OFA-Sys/Chinese-CLIP \n",
    "    \n",
    "**技术报告:** https://arxiv.org/abs/2211.01335\n",
    "\n",
    "**电商多模态图文检索挑战赛主页**: https://tianchi.aliyun.com/competition/entrance/532031/introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 说明：\n",
    "\n",
    "1. 基于此notebook，可以轻松实现利用**Chinese-CLIP**图文预训练双塔表征模型，在电商多模态图文检索挑战赛完成finetune及验证集/测试集预测\n",
    "2. 由于对比学习对于训练batch大小有一定的基本要求，此notebook运行至少**需要8GB显存**，请大家在GPU显存满足此要求的运行环境下下载并运行此notebook（[下载链接](https://github.com/OFA-Sys/Chinese-CLIP/blob/master/Chinese-CLIP-on-MUGE-Retrieval.ipynb)），尝试baseline流程。目前天池实验室Notebook环境暂不支持使用这个规模的显存，达摩院[ModelScope平台](https://modelscope.cn/models/damo/multi-modal_clip-vit-base-patch16_zh/summary)提供了一定的GPU机时（进入链接后右上角\"在Notebook打开\"），资源紧张的同学可以考虑利用\n",
    "3. 请保证环境Pytorch版本至少在1.8.0及以上\n",
    "4. Notebook中的流程较为简单，主要方便参赛选手上手跑通一个不错的base规模（视觉侧ViT-B/16，文本侧Roberta-base）baseline。如果要继续调优或改进模型，建议进一步详细参考github开源源码（尤其是[readme跨模态检索部分的文档](https://github.com/OFA-Sys/Chinese-CLIP#跨模态检索)）和Chinese-CLIP技术报告"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作\n",
    "### 1. Clone代码库并准备数据目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'Chinese-CLIP'...\n",
      "remote: Enumerating objects: 990, done.\u001b[K\n",
      "remote: Counting objects: 100% (364/364), done.\u001b[K\n",
      "remote: Compressing objects: 100% (131/131), done.\u001b[K\n",
      "remote: Total 990 (delta 328), reused 233 (delta 233), pack-reused 626\u001b[K\n",
      "Receiving objects: 100% (990/990), 402.11 KiB | 593.00 KiB/s, done.\n",
      "Resolving deltas: 100% (633/633), done.\n",
      "Updating files: 100% (54/54), done.\n",
      "\u001b[01;34m.\u001b[0m\n",
      "├── \u001b[01;34mChinese-CLIP\u001b[0m\n",
      "│   ├── \u001b[01;34massets\u001b[0m\n",
      "│   │   └── \u001b[00mChinese_CLIP_logo_tp_path.svg\u001b[0m\n",
      "│   ├── \u001b[01;34mcn_clip\u001b[0m\n",
      "│   │   ├── \u001b[01;34mclip\u001b[0m\n",
      "│   │   │   ├── \u001b[00mbert_tokenizer.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mconfiguration_bert.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00m__init__.py\u001b[0m\n",
      "│   │   │   ├── \u001b[01;34mmodel_configs\u001b[0m\n",
      "│   │   │   │   ├── \u001b[00mRBT3-chinese.json\u001b[0m\n",
      "│   │   │   │   ├── \u001b[00mRN50.json\u001b[0m\n",
      "│   │   │   │   ├── \u001b[00mRoBERTa-wwm-ext-base-chinese.json\u001b[0m\n",
      "│   │   │   │   ├── \u001b[00mRoBERTa-wwm-ext-large-chinese.json\u001b[0m\n",
      "│   │   │   │   ├── \u001b[00mViT-B-16.json\u001b[0m\n",
      "│   │   │   │   ├── \u001b[00mViT-B-32.json\u001b[0m\n",
      "│   │   │   │   ├── \u001b[00mViT-H-14.json\u001b[0m\n",
      "│   │   │   │   ├── \u001b[00mViT-L-14-336.json\u001b[0m\n",
      "│   │   │   │   └── \u001b[00mViT-L-14.json\u001b[0m\n",
      "│   │   │   ├── \u001b[00mmodeling_bert.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mmodel.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mutils.py\u001b[0m\n",
      "│   │   │   └── \u001b[00mvocab.txt\u001b[0m\n",
      "│   │   ├── \u001b[01;34meval\u001b[0m\n",
      "│   │   │   ├── \u001b[00mcvinw_zeroshot_templates.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mdata.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mevaluation.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mevaluation_tr.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mextract_features.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mimagenet_zeroshot_templates.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00m__init__.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mmake_topk_predictions.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mmake_topk_predictions_tr.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00mtransform_ir_annotation_to_tr.py\u001b[0m\n",
      "│   │   │   └── \u001b[00mzeroshot_evaluation.py\u001b[0m\n",
      "│   │   ├── \u001b[00m__init__.py\u001b[0m\n",
      "│   │   ├── \u001b[01;34mpreprocess\u001b[0m\n",
      "│   │   │   ├── \u001b[00mbuild_lmdb_dataset.py\u001b[0m\n",
      "│   │   │   ├── \u001b[00m__init__.py\u001b[0m\n",
      "│   │   │   └── \u001b[00mtransform_openai_pretrain_weights.py\u001b[0m\n",
      "│   │   └── \u001b[01;34mtraining\u001b[0m\n",
      "│   │       ├── \u001b[00mdata.py\u001b[0m\n",
      "│   │       ├── \u001b[00m__init__.py\u001b[0m\n",
      "│   │       ├── \u001b[00mlogger.py\u001b[0m\n",
      "│   │       ├── \u001b[00mmain.py\u001b[0m\n",
      "│   │       ├── \u001b[00mparams.py\u001b[0m\n",
      "│   │       ├── \u001b[00mscheduler.py\u001b[0m\n",
      "│   │       └── \u001b[00mtrain.py\u001b[0m\n",
      "│   ├── \u001b[01;34mexamples\u001b[0m\n",
      "│   │   └── \u001b[01;35mpokemon.jpeg\u001b[0m\n",
      "│   ├── \u001b[00mMIT-LICENSE.txt\u001b[0m\n",
      "│   ├── \u001b[00mREADME_En.md\u001b[0m\n",
      "│   ├── \u001b[00mREADME.md\u001b[0m\n",
      "│   ├── \u001b[00mrequirements.txt\u001b[0m\n",
      "│   ├── \u001b[00mResults.md\u001b[0m\n",
      "│   ├── \u001b[01;34mrun_scripts\u001b[0m\n",
      "│   │   ├── \u001b[00mflickr30k_finetune_vit-b-16_rbt-base_flip.sh\u001b[0m\n",
      "│   │   ├── \u001b[00mflickr30k_finetune_vit-b-16_rbt-base.sh\u001b[0m\n",
      "│   │   ├── \u001b[00mmuge_finetune_vit-b-16_rbt-base_flip.sh\u001b[0m\n",
      "│   │   ├── \u001b[00mmuge_finetune_vit-b-16_rbt-base.sh\u001b[0m\n",
      "│   │   └── \u001b[00mzeroshot_eval.sh\u001b[0m\n",
      "│   ├── \u001b[00msetup.py\u001b[0m\n",
      "│   ├── \u001b[00mzeroshot_dataset_en.md\u001b[0m\n",
      "│   └── \u001b[00mzeroshot_dataset.md\u001b[0m\n",
      "├── \u001b[00mChinese-CLIP-on-Retrieval.ipynb\u001b[0m\n",
      "└── \u001b[01;34mdatapath\u001b[0m\n",
      "    ├── \u001b[01;34mdatasets\u001b[0m\n",
      "    ├── \u001b[01;34mexperiments\u001b[0m\n",
      "    └── \u001b[01;34mpretrained_weights\u001b[0m\n",
      "\n",
      "14 directories, 54 files\n"
     ]
    }
   ],
   "source": [
    "# clone Chinese-CLIP代码库到用户目录(如果因网络问题卡在git clone这步，请interrupt该步并重试几次)：\n",
    "!git clone https://github.com/OFA-Sys/Chinese-CLIP.git\n",
    "\n",
    "# 创建一个datapath文件夹，用于存放数据集和Chinese-CLIP参数\n",
    "!mkdir -p datapath\n",
    "!mkdir -p datapath/pretrained_weights # 存放预训练参数\n",
    "!mkdir -p datapath/datasets # 存放数据集\n",
    "!mkdir -p datapath/experiments # 存放finetune参数和日志\n",
    "\n",
    "!tree ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 安装Chinese-CLIP相关依赖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple/\n",
      "Requirement already satisfied: numpy in /home/pai/lib/python3.6/site-packages (from -r Chinese-CLIP/requirements.txt (line 1)) (1.19.5)\n",
      "Requirement already satisfied: tqdm in /home/pai/lib/python3.6/site-packages (from -r Chinese-CLIP/requirements.txt (line 2)) (4.64.0)\n",
      "Requirement already satisfied: six in /home/pai/lib/python3.6/site-packages (from -r Chinese-CLIP/requirements.txt (line 3)) (1.16.0)\n",
      "Requirement already satisfied: timm in /home/pai/lib/python3.6/site-packages (from -r Chinese-CLIP/requirements.txt (line 4)) (0.6.12)\n",
      "Requirement already satisfied: lmdb==1.3.0 in /home/pai/lib/python3.6/site-packages (from -r Chinese-CLIP/requirements.txt (line 5)) (1.3.0)\n",
      "Requirement already satisfied: torch>=1.7.1 in /home/pai/lib/python3.6/site-packages (from -r Chinese-CLIP/requirements.txt (line 6)) (1.10.2+cu113)\n",
      "Requirement already satisfied: torchvision in /home/pai/lib/python3.6/site-packages (from -r Chinese-CLIP/requirements.txt (line 7)) (0.11.3+cu113)\n",
      "Requirement already satisfied: importlib-resources in /home/pai/lib/python3.6/site-packages (from tqdm->-r Chinese-CLIP/requirements.txt (line 2)) (5.4.0)\n",
      "Requirement already satisfied: huggingface-hub in /home/pai/lib/python3.6/site-packages (from timm->-r Chinese-CLIP/requirements.txt (line 4)) (0.4.0)\n",
      "Requirement already satisfied: pyyaml in /home/pai/lib/python3.6/site-packages (from timm->-r Chinese-CLIP/requirements.txt (line 4)) (5.4.1)\n",
      "Requirement already satisfied: dataclasses in /home/pai/lib/python3.6/site-packages (from torch>=1.7.1->-r Chinese-CLIP/requirements.txt (line 6)) (0.8)\n",
      "Requirement already satisfied: typing-extensions in /home/pai/lib/python3.6/site-packages (from torch>=1.7.1->-r Chinese-CLIP/requirements.txt (line 6)) (3.7.4.3)\n",
      "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /home/pai/lib/python3.6/site-packages (from torchvision->-r Chinese-CLIP/requirements.txt (line 7)) (8.4.0)\n",
      "Requirement already satisfied: requests in /home/pai/lib/python3.6/site-packages (from huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (2.18.4)\n",
      "Requirement already satisfied: importlib-metadata in /home/pai/lib/python3.6/site-packages (from huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (4.8.1)\n",
      "Requirement already satisfied: packaging>=20.9 in /home/pai/lib/python3.6/site-packages (from huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (21.3)\n",
      "Requirement already satisfied: filelock in /home/pai/lib/python3.6/site-packages (from huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (3.4.1)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /home/pai/lib/python3.6/site-packages (from importlib-resources->tqdm->-r Chinese-CLIP/requirements.txt (line 2)) (3.6.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/pai/lib/python3.6/site-packages (from packaging>=20.9->huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (3.0.9)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/pai/lib/python3.6/site-packages (from requests->huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (3.0.4)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in /home/pai/lib/python3.6/site-packages (from requests->huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (2.6)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /home/pai/lib/python3.6/site-packages (from requests->huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (1.22)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/pai/lib/python3.6/site-packages (from requests->huggingface-hub->timm->-r Chinese-CLIP/requirements.txt (line 4)) (2021.5.30)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# 安装Chinese-CLIP依赖包\n",
    "!pip install -r Chinese-CLIP/requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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      "Package                      Version\n",
      "---------------------------- ------------\n",
      "absl-py                      0.15.0\n",
      "aiohttp                      3.8.1\n",
      "aiosignal                    1.2.0\n",
      "alibabacloud-credentials     0.2.0\n",
      "alibabacloud-endpoint-util   0.0.3\n",
      "alibabacloud-gateway-spi     0.0.1\n",
      "alibabacloud-openapi-util    0.1.6\n",
      "alibabacloud-pai-dlc20201203 1.0.0\n",
      "alibabacloud-tea             0.2.9\n",
      "alibabacloud-tea-openapi     0.3.3\n",
      "alibabacloud-tea-util        0.3.5\n",
      "alibabacloud-tea-xml         0.0.2\n",
      "alipai                       0.1.7\n",
      "aliyun-log-python-sdk        0.7.9\n",
      "aliyun-python-sdk-core       2.13.36\n",
      "aliyun-python-sdk-kms        2.15.0\n",
      "aliyun-python-sdk-sts        3.1.0\n",
      "apache-beam                  2.15.0\n",
      "apache-flink                 1.10.1\n",
      "argcomplete                  2.0.0\n",
      "argon2-cffi                  21.3.0\n",
      "argon2-cffi-bindings         21.2.0\n",
      "asn1crypto                   0.24.0\n",
      "astor                        0.8.1\n",
      "astroid                      2.11.5\n",
      "astunparse                   1.6.3\n",
      "async-generator              1.10\n",
      "async-timeout                4.0.2\n",
      "asynctest                    0.13.0\n",
      "attrs                        21.4.0\n",
      "autopep8                     1.6.0\n",
      "avro-python3                 1.9.1\n",
      "backcall                     0.2.0\n",
      "backports.zoneinfo           0.2.1\n",
      "bleach                       4.1.0\n",
      "brotlipy                     0.7.0\n",
      "cached-property              1.5.2\n",
      "cachetools                   4.2.4\n",
      "certifi                      2021.5.30\n",
      "cffi                         1.15.0\n",
      "chardet                      3.0.4\n",
      "charset-normalizer           2.0.12\n",
      "clang                        5.0\n",
      "cloudpickle                  1.2.2\n",
      "colorama                     0.4.4\n",
      "common-io                    0.3.0\n",
      "conda                        4.10.3\n",
      "conda-package-handling       1.7.3\n",
      "configparser                 5.2.0\n",
      "contextlib2                  21.6.0\n",
      "crcmod                       1.7\n",
      "cryptography                 37.0.2\n",
      "cvxopt                       1.3.0\n",
      "cycler                       0.11.0\n",
      "Cython                       0.29.24\n",
      "dataclasses                  0.8\n",
      "dateparser                   1.1.1\n",
      "decorator                    4.4.2\n",
      "defusedxml                   0.7.1\n",
      "deprecation                  2.1.0\n",
      "dill                         0.2.9\n",
      "docopt                       0.6.2\n",
      "eas-prediction               0.13\n",
      "easy-rec                     0.1.6\n",
      "elastic-transport            8.1.2\n",
      "elasticsearch                8.2.0\n",
      "entrypoints                  0.4\n",
      "fastavro                     0.21.24\n",
      "filelock                     3.4.1\n",
      "flake8                       4.0.1\n",
      "flatbuffers                  1.12\n",
      "frozenlist                   1.2.0\n",
      "future                       0.18.2\n",
      "gast                         0.4.0\n",
      "google-auth                  1.35.0\n",
      "google-auth-oauthlib         0.4.6\n",
      "google-pasta                 0.2.0\n",
      "graphviz                     0.19.1\n",
      "grpcio                       1.46.3\n",
      "h5py                         3.1.0\n",
      "hdfs                         2.7.0\n",
      "htmlmin                      0.1.12\n",
      "httplib2                     0.12.0\n",
      "huggingface-hub              0.4.0\n",
      "hyperopt                     0.1.2\n",
      "idna                         2.6\n",
      "idna-ssl                     1.1.0\n",
      "ImageHash                    4.2.1\n",
      "importlib-metadata           4.8.1\n",
      "importlib-resources          5.4.0\n",
      "ipykernel                    5.5.6\n",
      "ipython                      7.16.3\n",
      "ipython-genutils             0.2.0\n",
      "ipywidgets                   7.7.0\n",
      "isort                        5.10.1\n",
      "jedi                         0.17.2\n",
      "Jinja2                       3.0.3\n",
      "jmespath                     0.10.0\n",
      "joblib                       1.1.0\n",
      "json-tricks                  3.15.5\n",
      "jsonschema                   4.0.0\n",
      "jupyter-client               7.1.2\n",
      "jupyter-core                 4.9.2\n",
      "jupyterlab-pygments          0.1.2\n",
      "jupyterlab-widgets           1.1.0\n",
      "keras                        2.6.0\n",
      "Keras-Preprocessing          1.1.2\n",
      "kiwisolver                   1.3.1\n",
      "lazy-object-proxy            1.7.1\n",
      "llvmlite                     0.36.0\n",
      "lmdb                         1.3.0\n",
      "mamba                        0.15.3\n",
      "Markdown                     3.3.7\n",
      "MarkupSafe                   2.0.1\n",
      "matplotlib                   3.3.4\n",
      "mccabe                       0.6.1\n",
      "minio                        7.1.8\n",
      "missingno                    0.4.2\n",
      "mistune                      0.8.4\n",
      "mock                         2.0.0\n",
      "multidict                    5.2.0\n",
      "multimethod                  1.4\n",
      "nbclient                     0.5.9\n",
      "nbconvert                    6.0.7\n",
      "nbformat                     5.1.3\n",
      "nest-asyncio                 1.5.5\n",
      "networkx                     2.7.1\n",
      "notebook                     6.4.10\n",
      "numba                        0.53.1\n",
      "numpy                        1.19.5\n",
      "oauth2client                 3.0.0\n",
      "oauthlib                     3.2.0\n",
      "olefile                      0.46\n",
      "opencv-contrib-python        4.5.5.64\n",
      "opencv-python                4.5.5.64\n",
      "opt-einsum                   3.3.0\n",
      "oss2                         2.15.0\n",
      "packaging                    21.3\n",
      "pai-nni                      2.6\n",
      "pandas                       1.1.5\n",
      "pandas-profiling             3.1.0\n",
      "pandocfilters                1.5.0\n",
      "parso                        0.7.1\n",
      "patsy                        0.5.2\n",
      "pbr                          5.9.0\n",
      "pexpect                      4.8.0\n",
      "phik                         0.11.2\n",
      "pickleshare                  0.7.5\n",
      "Pillow                       8.4.0\n",
      "pip                          21.3.1\n",
      "platformdirs                 2.4.0\n",
      "plotly                       5.8.0\n",
      "prettytable                  2.5.0\n",
      "prometheus-client            0.14.1\n",
      "prompt-toolkit               3.0.29\n",
      "protobuf                     3.19.4\n",
      "psutil                       5.9.1\n",
      "ptyprocess                   0.7.0\n",
      "py4j                         0.10.8.1\n",
      "pyalink-public               1.5.1\n",
      "pyarrow                      0.13.0\n",
      "pyasn1                       0.4.8\n",
      "pyasn1-modules               0.2.8\n",
      "pybind11                     2.7.0\n",
      "pycodestyle                  2.8.0\n",
      "pycosat                      0.6.3\n",
      "pycparser                    2.21\n",
      "pycryptodome                 3.14.1\n",
      "pydantic                     1.8.2\n",
      "pydot                        1.4.2\n",
      "pyflakes                     2.4.0\n",
      "Pygments                     2.12.0\n",
      "pylint                       2.13.9\n",
      "pymongo                      3.12.3\n",
      "pyodps                       0.11.0\n",
      "pyOpenSSL                    18.0.0\n",
      "pyparsing                    3.0.9\n",
      "pyrsistent                   0.18.0\n",
      "PySocks                      1.6.8\n",
      "python-dateutil              2.8.2\n",
      "PythonWebHDFS                0.2.3\n",
      "pytz                         2022.1\n",
      "pytz-deprecation-shim        0.1.0.post0\n",
      "PyWavelets                   1.1.1\n",
      "PyYAML                       5.4.1\n",
      "pyzmq                        23.0.0\n",
      "regex                        2022.3.2\n",
      "requests                     2.18.4\n",
      "requests-oauthlib            1.3.1\n",
      "responses                    0.17.0\n",
      "rsa                          4.8\n",
      "ruamel_yaml                  0.15.37\n",
      "ruamel-yaml-conda            0.15.100\n",
      "Rx                           3.2.0\n",
      "schema                       0.7.5\n",
      "scikit-learn                 0.24.2\n",
      "scipy                        1.5.4\n",
      "seaborn                      0.11.2\n",
      "Send2Trash                   1.8.0\n",
      "setuptools                   58.0.4\n",
      "simplejson                   3.17.6\n",
      "six                          1.16.0\n",
      "statsmodels                  0.12.2\n",
      "tangled-up-in-unicode        0.1.0\n",
      "tenacity                     8.0.1\n",
      "tensorboard                  2.6.0\n",
      "tensorboard-data-server      0.6.1\n",
      "tensorboard-plugin-wit       1.8.1\n",
      "tensorflow                   2.6.2\n",
      "tensorflow-estimator         2.6.0\n",
      "tensorflow-gpu               2.6.0\n",
      "tensorflow-io                0.21.0\n",
      "tensorflow-io-gcs-filesystem 0.21.0\n",
      "termcolor                    1.1.0\n",
      "terminado                    0.13.0\n",
      "testpath                     0.6.0\n",
      "threadpoolctl                3.1.0\n",
      "timm                         0.6.12\n",
      "toml                         0.10.2\n",
      "tomli                        1.2.3\n",
      "torch                        1.10.2+cu113\n",
      "torchaudio                   0.10.2+cu113\n",
      "torchvision                  0.11.3+cu113\n",
      "tornado                      6.1\n",
      "tqdm                         4.64.0\n",
      "traitlets                    4.3.3\n",
      "typed-ast                    1.5.4\n",
      "typing-extensions            3.7.4.3\n",
      "tzdata                       2022.1\n",
      "tzlocal                      4.2\n",
      "urllib3                      1.22\n",
      "visions                      0.7.4\n",
      "wcwidth                      0.2.5\n",
      "webencodings                 0.5.1\n",
      "websockets                   9.1\n",
      "Werkzeug                     2.0.3\n",
      "wheel                        0.37.1\n",
      "widgetsnbextension           3.6.0\n",
      "wrapt                        1.12.1\n",
      "xgboost                      1.5.2\n",
      "xlrd                         2.0.1\n",
      "xmltodict                    0.13.0\n",
      "yarl                         1.7.2\n",
      "yq                           2.13.0\n",
      "zipp                         3.6.0\n"
     ]
    }
   ],
   "source": [
    "# 查看当前kernel下已安装的包  list packages\n",
    "!pip list --format=columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 下载Chinese-CLIP预训练参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2023-01-13T07:38:37.732489Z",
     "iopub.status.busy": "2023-01-13T07:38:37.732172Z",
     "iopub.status.idle": "2023-01-13T07:39:18.308756Z",
     "shell.execute_reply": "2023-01-13T07:39:18.307760Z",
     "shell.execute_reply.started": "2023-01-13T07:38:37.732449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "# 下载base规模Chinese-CLIP预训练参数：\n",
    "!wget https://www.modelscope.cn/models/AI-ModelScope/chinese-clip-vit-base-patch16/resolve/master/clip_cn_vit-b-16.pt\n",
    "!mv clip_cn_vit-b-16.pt datapath/pretrained_weights/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 预处理数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2023-01-13T07:41:28.580949Z",
     "iopub.status.busy": "2023-01-13T07:41:28.580661Z",
     "iopub.status.idle": "2023-01-13T07:43:34.116492Z",
     "shell.execute_reply": "2023-01-13T07:43:34.115666Z",
     "shell.execute_reply.started": "2023-01-13T07:41:28.580909Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-01-13 15:41:28--  https://huggingface.co/datasets/OFA-Sys/chinese-clip-eval/resolve/main/MUGE.zip\n",
      "...\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2166554702 (2.0G) [application/zip]\n",
      "Saving to: ‘MUGE.zip’\n",
      "\n",
      "MUGE.zip            100%[===================>]   2.02G  26.7MB/s    in 89s     \n",
      "\n",
      "2023-01-13 15:42:58 (23.1 MB/s) - ‘MUGE.zip’ saved [2166554702/2166554702]\n",
      "\n",
      "/mnt/workspace/tianchi/datapath/datasets\n",
      "Archive:  MUGE.zip\n",
      "   creating: MUGE/\n",
      "   creating: MUGE/lmdb/\n",
      "   creating: MUGE/lmdb/train/\n",
      "   creating: MUGE/lmdb/train/imgs/\n",
      "  inflating: MUGE/lmdb/train/imgs/data.mdb  \n",
      "  inflating: MUGE/lmdb/train/imgs/lock.mdb  \n",
      "   creating: MUGE/lmdb/train/pairs/\n",
      "  inflating: MUGE/lmdb/train/pairs/data.mdb  \n",
      "  inflating: MUGE/lmdb/train/pairs/lock.mdb  \n",
      "   creating: MUGE/lmdb/valid/\n",
      "   creating: MUGE/lmdb/valid/imgs/\n",
      "  inflating: MUGE/lmdb/valid/imgs/data.mdb  \n",
      "  inflating: MUGE/lmdb/valid/imgs/lock.mdb  \n",
      "   creating: MUGE/lmdb/valid/pairs/\n",
      "  inflating: MUGE/lmdb/valid/pairs/data.mdb  \n",
      "  inflating: MUGE/lmdb/valid/pairs/lock.mdb  \n",
      "  inflating: MUGE/train_imgs.tsv     \n",
      "  inflating: MUGE/train_texts.jsonl  \n",
      "  inflating: MUGE/valid_imgs.tsv     \n",
      "  inflating: MUGE/valid_texts.jsonl  \n",
      "/mnt/workspace/tianchi\n",
      "\u001b[01;34mdatapath/\u001b[0m\n",
      "├── \u001b[01;34mdatasets\u001b[0m\n",
      "│   ├── \u001b[01;34mMUGE\u001b[0m\n",
      "│   │   ├── \u001b[01;34mlmdb\u001b[0m\n",
      "│   │   │   ├── \u001b[01;34mtrain\u001b[0m\n",
      "│   │   │   │   ├── \u001b[01;34mimgs\u001b[0m\n",
      "│   │   │   │   │   ├── \u001b[00mdata.mdb\u001b[0m\n",
      "│   │   │   │   │   └── \u001b[00mlock.mdb\u001b[0m\n",
      "│   │   │   │   └── \u001b[01;34mpairs\u001b[0m\n",
      "│   │   │   │       ├── \u001b[00mdata.mdb\u001b[0m\n",
      "│   │   │   │       └── \u001b[00mlock.mdb\u001b[0m\n",
      "│   │   │   └── \u001b[01;34mvalid\u001b[0m\n",
      "│   │   │       ├── \u001b[01;34mimgs\u001b[0m\n",
      "│   │   │       │   ├── \u001b[00mdata.mdb\u001b[0m\n",
      "│   │   │       │   └── \u001b[00mlock.mdb\u001b[0m\n",
      "│   │   │       └── \u001b[01;34mpairs\u001b[0m\n",
      "│   │   │           ├── \u001b[00mdata.mdb\u001b[0m\n",
      "│   │   │           └── \u001b[00mlock.mdb\u001b[0m\n",
      "│   │   ├── \u001b[00mtrain_imgs.tsv\u001b[0m\n",
      "│   │   ├── \u001b[00mtrain_texts.jsonl\u001b[0m\n",
      "│   │   ├── \u001b[00mvalid_imgs.tsv\u001b[0m\n",
      "│   │   └── \u001b[00mvalid_texts.jsonl\u001b[0m\n",
      "│   └── \u001b[01;31mMUGE.zip\u001b[0m\n",
      "├── \u001b[01;34mexperiments\u001b[0m\n",
      "└── \u001b[01;34mpretrained_weights\u001b[0m\n",
      "    └── \u001b[00mclip_cn_vit-b-16.pt\u001b[0m\n",
      "\n",
      "11 directories, 14 files\n"
     ]
    }
   ],
   "source": [
    "# 详细的预处理数据流程，请参见github readme: https://github.com/OFA-Sys/Chinese-CLIP#数据集格式预处理\n",
    "# 我们这里方便起见，直接下载已经预处理好（处理成LMDB格式）的电商检索数据集\n",
    "!wget https://huggingface.co/datasets/OFA-Sys/chinese-clip-eval/resolve/main/MUGE.zip\n",
    "!mv MUGE.zip datapath/datasets/\n",
    "%cd datapath/datasets/\n",
    "!unzip MUGE.zip\n",
    "%cd ../..\n",
    "!tree datapath/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-13T07:43:39.365603Z",
     "iopub.status.busy": "2023-01-13T07:43:39.365196Z",
     "iopub.status.idle": "2023-01-13T07:43:39.911726Z",
     "shell.execute_reply": "2023-01-13T07:43:39.911099Z",
     "shell.execute_reply.started": "2023-01-13T07:43:39.365556Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"text_id\": 248816, \"text\": \"圣诞 抱枕\", \"image_ids\": [1006938, 561749, 936929, 286314, 141999, 183846]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7FD2B7E261D0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7FD29B384198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7FD29B384208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看数据样例\n",
    "!head -n 1 datapath/datasets/MUGE/valid_texts.jsonl # 验证集第一条样本\n",
    "\n",
    "import lmdb\n",
    "import base64\n",
    "from io import BytesIO\n",
    "from PIL import Image\n",
    "\n",
    "image_ids = [286314, 141999, 183846]\n",
    "\n",
    "lmdb_imgs = \"datapath/datasets/MUGE/lmdb/valid/imgs\"\n",
    "env_imgs = lmdb.open(lmdb_imgs, readonly=True, create=False, lock=False, readahead=False, meminit=False)\n",
    "txn_imgs = env_imgs.begin(buffers=True)\n",
    "for image_id in image_ids:\n",
    "    image_b64 = txn_imgs.get(\"{}\".format(image_id).encode('utf-8')).tobytes()\n",
    "    img = Image.open(BytesIO(base64.urlsafe_b64decode(image_b64)))\n",
    "    img.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 运行finetune训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-13T07:43:47.598366Z",
     "iopub.status.busy": "2023-01-13T07:43:47.598084Z",
     "iopub.status.idle": "2023-01-13T07:43:47.603552Z",
     "shell.execute_reply": "2023-01-13T07:43:47.602812Z",
     "shell.execute_reply.started": "2023-01-13T07:43:47.598340Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/workspace/tianchi/Chinese-CLIP\n"
     ]
    }
   ],
   "source": [
    "# 进入代码工作区目录\n",
    "%cd Chinese-CLIP/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们开始运行finetune，说明如下：\n",
    "+ 下面的代码改编自代码库中的shell运行脚本: Chinese-CLIP/run_scripts/muge_finetune_vit-b-16_rbt-base.sh\n",
    "+ **详细的finetune各配置项，请参见: https://github.com/OFA-Sys/Chinese-CLIP#模型finetune**\n",
    "+ 执行训练的python代码，请参见: Chinese-CLIP/cn_clip/training/main.py\n",
    "+ 模型实现的python代码，请参见: Chinese-CLIP/cn_clip/clip/model.py 以及比赛官方学习视频对于模型实现的介绍\n",
    "+ 按照如下的超参进行finetune，要求机器需要至少达到8G显存，finetune时间大约需要40min左右（V100上测试）\n",
    "+ **请注意finetune中间给出的验证集准确率，是验证集batch内部，图文对之间的Recall@1正确率，与比赛指标要求的从整个验证集/测试集图片池召回的Recall@1并不是同一个指标。这里仅用于观察训练趋势，如果要评测模型效果，请完整运行下文的特征提取、KNN召回和计算Recall流程**\n",
    "+ 对比学习的训练收敛和稳定性和总batch size相关。如您使用较小的batch size（比如我们这里的48 per-GPU * 1 GPU），建议使用较小的学习率（如我们这里的3e-6）。我们推荐使用更多的GPU和更大的batch size以取得更好的效果。\n",
    "+ 训练完成后如果输出打印EOFError，无视即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2023-01-14T12:31:56.709738Z",
     "iopub.status.busy": "2023-01-14T12:31:56.709466Z",
     "iopub.status.idle": "2023-01-14T12:31:56.720085Z",
     "shell.execute_reply": "2023-01-14T12:31:56.719253Z",
     "shell.execute_reply.started": "2023-01-14T12:31:56.709713Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;python3 -m torch.distributed.launch --nproc_per_node=1 --nnodes=1 --node_rank=0       --master_addr=localhost --master_port=8514 cn_clip/training/main.py       --train-data=../datapath//datasets/MUGE/lmdb/train       --val-data=../datapath//datasets/MUGE/lmdb/valid       --num-workers=4       --valid-num-workers=4       --resume=../datapath//pretrained_weights/clip_cn_vit-b-16.pt       --reset-data-offset       --reset-optimizer       --logs=../datapath//experiments/       --name=muge_finetune_vit-b-16_roberta-base_bs48_1gpu       --save-step-frequency=999999       --save-epoch-frequency=1       --log-interval=10       --report-training-batch-acc       --context-length=52       --warmup=100       --batch-size=48       --valid-batch-size=48       --valid-step-interval=1000       --valid-epoch-interval=1       --lr=3e-06       --wd=0.001       --max-epochs=1       --vision-model=ViT-B-16       --use-augment       --grad-checkpointing       --text-model=RoBERTa-wwm-ext-base-chinese\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 准备finetune相关配置，详见https://github.com/OFA-Sys/Chinese-CLIP#模型finetune\n",
    "# 指定机器数 & 卡数\n",
    "GPUS_PER_NODE=1 # 卡数\n",
    "WORKER_CNT=1 # 机器数\n",
    "MASTER_ADDR=\"localhost\"\n",
    "MASTER_PORT=8514 # 同台机器同时起多个任务，请分别分配不同的端口号\n",
    "RANK=0 \n",
    "\n",
    "# 刚刚创建过的目录，存放了预训练参数和预处理好的数据集\n",
    "DATAPATH=\"../datapath/\"\n",
    "\n",
    "# 指定LMDB格式的训练集和验证集路径（存放了LMDB格式的图片和图文对数据）\n",
    "train_data=f\"{DATAPATH}/datasets/MUGE/lmdb/train\"\n",
    "val_data=f\"{DATAPATH}/datasets/MUGE/lmdb/valid\"\n",
    "num_workers=4 # 训练集pytorch dataloader的进程数，设置为>0，以减小训练时读取数据的时间开销\n",
    "valid_num_workers=4 # 验证集pytorch dataloader的进程数，设置为>0，以减小验证时读取数据的时间开销\n",
    "\n",
    "# 指定刚刚下载好的Chinese-CLIP预训练权重的路径\n",
    "resume=f\"{DATAPATH}/pretrained_weights/clip_cn_vit-b-16.pt\"\n",
    "reset_data_offset=\"--reset-data-offset\" # 从头读取训练数据\n",
    "reset_optimizer=\"--reset-optimizer\" # 重新初始化AdamW优化器\n",
    "\n",
    "# 指定输出相关配置\n",
    "output_base_dir=f\"{DATAPATH}/experiments/\"\n",
    "name=\"muge_finetune_vit-b-16_roberta-base_bs48_1gpu\" # finetune超参、日志、ckpt将保存在../datapath/experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/\n",
    "save_step_frequency=999999 # disable it\n",
    "save_epoch_frequency=1 # 每轮保存一个finetune ckpt\n",
    "log_interval=10 # 日志打印间隔步数\n",
    "report_training_batch_acc=\"--report-training-batch-acc\" # 训练中，报告训练batch的in-batch准确率\n",
    "\n",
    "# 指定训练超参数\n",
    "context_length=52 # 序列长度，这里指定为Chinese-CLIP默认的52\n",
    "warmup=100 # warmup步数\n",
    "batch_size=48 # 训练单卡batch size\n",
    "valid_batch_size=48 # 验证单卡batch size\n",
    "lr=3e-6 # 学习率，因为这里我们使用的对比学习batch size很小，所以对应的学习率也调低一些\n",
    "wd=0.001 # weight decay\n",
    "max_epochs=1 # 训练轮数，也可通过--max-steps指定训练步数\n",
    "valid_step_interval=1000 # 验证步数间隔\n",
    "valid_epoch_interval=1 # 验证轮数间隔\n",
    "vision_model=\"ViT-B-16\" # 指定视觉侧结构为ViT-B/16\n",
    "text_model=\"RoBERTa-wwm-ext-base-chinese\" # 指定文本侧结构为RoBERTa-base\n",
    "use_augment=\"--use-augment\" # 对图像使用数据增强\n",
    "grad_checkpointing=\"--grad-checkpointing\" # 激活重计算策略，用更多训练时间换取更小的显存开销\n",
    "\n",
    "run_command = \"export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\" + \\\n",
    "f\"\"\"\n",
    "python3 -m torch.distributed.launch --nproc_per_node={GPUS_PER_NODE} --nnodes={WORKER_CNT} --node_rank={RANK} \\\n",
    "      --master_addr={MASTER_ADDR} --master_port={MASTER_PORT} cn_clip/training/main.py \\\n",
    "      --train-data={train_data} \\\n",
    "      --val-data={val_data} \\\n",
    "      --num-workers={num_workers} \\\n",
    "      --valid-num-workers={valid_num_workers} \\\n",
    "      --resume={resume} \\\n",
    "      {reset_data_offset} \\\n",
    "      {reset_optimizer} \\\n",
    "      --logs={output_base_dir} \\\n",
    "      --name={name} \\\n",
    "      --save-step-frequency={save_step_frequency} \\\n",
    "      --save-epoch-frequency={save_epoch_frequency} \\\n",
    "      --log-interval={log_interval} \\\n",
    "      {report_training_batch_acc} \\\n",
    "      --context-length={context_length} \\\n",
    "      --warmup={warmup} \\\n",
    "      --batch-size={batch_size} \\\n",
    "      --valid-batch-size={valid_batch_size} \\\n",
    "      --valid-step-interval={valid_step_interval} \\\n",
    "      --valid-epoch-interval={valid_epoch_interval} \\\n",
    "      --lr={lr} \\\n",
    "      --wd={wd} \\\n",
    "      --max-epochs={max_epochs} \\\n",
    "      --vision-model={vision_model} \\\n",
    "      {use_augment} \\\n",
    "      {grad_checkpointing} \\\n",
    "      --text-model={text_model}\n",
    "\"\"\".lstrip()\n",
    "print(run_command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-14T12:32:00.252547Z",
     "iopub.status.busy": "2023-01-14T12:32:00.252205Z",
     "iopub.status.idle": "2023-01-14T13:18:44.855512Z",
     "shell.execute_reply": "2023-01-14T13:18:44.854585Z",
     "shell.execute_reply.started": "2023-01-14T12:32:00.252513Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/pai/lib/python3.6/site-packages/torch/distributed/launch.py:186: FutureWarning: The module torch.distributed.launch is deprecated\n",
      "and will be removed in future. Use torchrun.\n",
      "Note that --use_env is set by default in torchrun.\n",
      "If your script expects `--local_rank` argument to be set, please\n",
      "change it to read from `os.environ['LOCAL_RANK']` instead. See \n",
      "https://pytorch.org/docs/stable/distributed.html#launch-utility for \n",
      "further instructions\n",
      "\n",
      "  FutureWarning,\n",
      "/home/pai/lib/python3.6/site-packages/OpenSSL/crypto.py:12: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography and will be removed in a future release.\n",
      "  from cryptography import x509\n",
      "Loading vision model config from cn_clip/clip/model_configs/ViT-B-16.json\n",
      "Loading text model config from cn_clip/clip/model_configs/RoBERTa-wwm-ext-base-chinese.json\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 | Grad-checkpointing activated.\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 | train LMDB file contains 129380 images and 250314 pairs.\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 | val LMDB file contains 29806 images and 30588 pairs.\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 | Params:\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   aggregate: True\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   batch_size: 48\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   bert_weight_path: None\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   beta1: 0.9\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   beta2: 0.98\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   checkpoint_path: ../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   clip_weight_path: None\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   context_length: 52\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   debug: False\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   device: cuda:0\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   eps: 1e-06\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   freeze_vision: False\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   grad_checkpointing: True\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   local_device_rank: 0\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   local_rank: 0\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   log_interval: 10\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   log_level: 20\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   log_path: ../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/out_2023-01-14-12-32-03.log\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   logs: ../datapath//experiments/\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   lr: 3e-06\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   mask_ratio: 0\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   max_epochs: 1\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   max_steps: 5215\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   name: muge_finetune_vit-b-16_roberta-base_bs48_1gpu\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   num_workers: 4\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   precision: amp\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   rank: 0\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   report_training_batch_acc: True\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   reset_data_offset: True\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   reset_optimizer: True\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   resume: ../datapath//pretrained_weights/clip_cn_vit-b-16.pt\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   save_epoch_frequency: 1\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   save_step_frequency: 999999\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   seed: 123\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   skip_aggregate: False\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   skip_scheduler: False\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   text_model: RoBERTa-wwm-ext-base-chinese\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   train_data: ../datapath//datasets/MUGE/lmdb/train\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   use_augment: True\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   use_bn_sync: False\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   val_data: ../datapath//datasets/MUGE/lmdb/valid\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   valid_batch_size: 48\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   valid_epoch_interval: 1\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   valid_num_workers: 4\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   valid_step_interval: 1000\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   vision_model: ViT-B-16\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   warmup: 100\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   wd: 0.001\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 |   world_size: 1\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 | Use GPU: 0 for training\n",
      "2023-01-14,20:32:11 | INFO | Rank 0 | => begin to load checkpoint '../datapath//pretrained_weights/clip_cn_vit-b-16.pt'\n",
      "2023-01-14,20:32:12 | INFO | Rank 0 | => loaded checkpoint '../datapath//pretrained_weights/clip_cn_vit-b-16.pt' (epoch 15 @ 0 steps)\n",
      "2023-01-14,20:32:14 | INFO | Rank 0 | Reducer buckets have been rebuilt in this iteration.\n",
      "2023-01-14,20:32:17 | INFO | Rank 0 | Global Steps: 10/5215 | Train Epoch: 1 [480/250320 (0%)] | Loss: 0.551259 | Image2Text Acc: 81.25 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:21 | INFO | Rank 0 | Global Steps: 20/5215 | Train Epoch: 1 [960/250320 (0%)] | Loss: 0.655716 | Image2Text Acc: 79.17 | Text2Image Acc: 79.17 | Data Time: 0.020s | Batch Time: 0.360s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:24 | INFO | Rank 0 | Global Steps: 30/5215 | Train Epoch: 1 [1440/250320 (1%)] | Loss: 0.894897 | Image2Text Acc: 81.25 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.360s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:28 | INFO | Rank 0 | Global Steps: 40/5215 | Train Epoch: 1 [1920/250320 (1%)] | Loss: 0.891962 | Image2Text Acc: 81.25 | Text2Image Acc: 75.00 | Data Time: 0.020s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:31 | INFO | Rank 0 | Global Steps: 50/5215 | Train Epoch: 1 [2400/250320 (1%)] | Loss: 0.711004 | Image2Text Acc: 77.08 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.360s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:35 | INFO | Rank 0 | Global Steps: 60/5215 | Train Epoch: 1 [2880/250320 (1%)] | Loss: 0.602132 | Image2Text Acc: 81.25 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:39 | INFO | Rank 0 | Global Steps: 70/5215 | Train Epoch: 1 [3360/250320 (1%)] | Loss: 0.429699 | Image2Text Acc: 85.42 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:42 | INFO | Rank 0 | Global Steps: 80/5215 | Train Epoch: 1 [3840/250320 (2%)] | Loss: 0.602740 | Image2Text Acc: 81.25 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:46 | INFO | Rank 0 | Global Steps: 90/5215 | Train Epoch: 1 [4320/250320 (2%)] | Loss: 0.612829 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.024s | Batch Time: 0.372s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:50 | INFO | Rank 0 | Global Steps: 100/5215 | Train Epoch: 1 [4800/250320 (2%)] | Loss: 0.393124 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:53 | INFO | Rank 0 | Global Steps: 110/5215 | Train Epoch: 1 [5280/250320 (2%)] | Loss: 0.642643 | Image2Text Acc: 83.33 | Text2Image Acc: 81.25 | Data Time: 0.018s | Batch Time: 0.360s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:32:57 | INFO | Rank 0 | Global Steps: 120/5215 | Train Epoch: 1 [5760/250320 (2%)] | Loss: 0.751711 | Image2Text Acc: 79.17 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:01 | INFO | Rank 0 | Global Steps: 130/5215 | Train Epoch: 1 [6240/250320 (2%)] | Loss: 0.654346 | Image2Text Acc: 85.42 | Text2Image Acc: 81.25 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:04 | INFO | Rank 0 | Global Steps: 140/5215 | Train Epoch: 1 [6720/250320 (3%)] | Loss: 0.744869 | Image2Text Acc: 77.08 | Text2Image Acc: 81.25 | Data Time: 0.023s | Batch Time: 0.379s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:08 | INFO | Rank 0 | Global Steps: 150/5215 | Train Epoch: 1 [7200/250320 (3%)] | Loss: 0.735694 | Image2Text Acc: 83.33 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.370s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:12 | INFO | Rank 0 | Global Steps: 160/5215 | Train Epoch: 1 [7680/250320 (3%)] | Loss: 0.583934 | Image2Text Acc: 79.17 | Text2Image Acc: 79.17 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:15 | INFO | Rank 0 | Global Steps: 170/5215 | Train Epoch: 1 [8160/250320 (3%)] | Loss: 0.532281 | Image2Text Acc: 83.33 | Text2Image Acc: 79.17 | Data Time: 0.023s | Batch Time: 0.365s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:19 | INFO | Rank 0 | Global Steps: 180/5215 | Train Epoch: 1 [8640/250320 (3%)] | Loss: 0.340221 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:23 | INFO | Rank 0 | Global Steps: 190/5215 | Train Epoch: 1 [9120/250320 (4%)] | Loss: 0.508061 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:26 | INFO | Rank 0 | Global Steps: 200/5215 | Train Epoch: 1 [9600/250320 (4%)] | Loss: 0.459738 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:30 | INFO | Rank 0 | Global Steps: 210/5215 | Train Epoch: 1 [10080/250320 (4%)] | Loss: 0.673064 | Image2Text Acc: 83.33 | Text2Image Acc: 75.00 | Data Time: 0.021s | Batch Time: 0.379s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:34 | INFO | Rank 0 | Global Steps: 220/5215 | Train Epoch: 1 [10560/250320 (4%)] | Loss: 0.335541 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.021s | Batch Time: 0.369s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:37 | INFO | Rank 0 | Global Steps: 230/5215 | Train Epoch: 1 [11040/250320 (4%)] | Loss: 0.246524 | Image2Text Acc: 93.75 | Text2Image Acc: 97.92 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:41 | INFO | Rank 0 | Global Steps: 240/5215 | Train Epoch: 1 [11520/250320 (5%)] | Loss: 0.867559 | Image2Text Acc: 75.00 | Text2Image Acc: 77.08 | Data Time: 0.020s | Batch Time: 0.375s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:45 | INFO | Rank 0 | Global Steps: 250/5215 | Train Epoch: 1 [12000/250320 (5%)] | Loss: 0.892492 | Image2Text Acc: 77.08 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:48 | INFO | Rank 0 | Global Steps: 260/5215 | Train Epoch: 1 [12480/250320 (5%)] | Loss: 0.289513 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:52 | INFO | Rank 0 | Global Steps: 270/5215 | Train Epoch: 1 [12960/250320 (5%)] | Loss: 0.897694 | Image2Text Acc: 77.08 | Text2Image Acc: 77.08 | Data Time: 0.021s | Batch Time: 0.371s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:56 | INFO | Rank 0 | Global Steps: 280/5215 | Train Epoch: 1 [13440/250320 (5%)] | Loss: 0.518537 | Image2Text Acc: 81.25 | Text2Image Acc: 81.25 | Data Time: 0.020s | Batch Time: 0.364s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:33:59 | INFO | Rank 0 | Global Steps: 290/5215 | Train Epoch: 1 [13920/250320 (6%)] | Loss: 0.797377 | Image2Text Acc: 72.92 | Text2Image Acc: 77.08 | Data Time: 0.021s | Batch Time: 0.364s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:03 | INFO | Rank 0 | Global Steps: 300/5215 | Train Epoch: 1 [14400/250320 (6%)] | Loss: 0.523968 | Image2Text Acc: 77.08 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.368s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:07 | INFO | Rank 0 | Global Steps: 310/5215 | Train Epoch: 1 [14880/250320 (6%)] | Loss: 0.480399 | Image2Text Acc: 89.58 | Text2Image Acc: 77.08 | Data Time: 0.019s | Batch Time: 0.383s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:11 | INFO | Rank 0 | Global Steps: 320/5215 | Train Epoch: 1 [15360/250320 (6%)] | Loss: 0.580155 | Image2Text Acc: 83.33 | Text2Image Acc: 75.00 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:14 | INFO | Rank 0 | Global Steps: 330/5215 | Train Epoch: 1 [15840/250320 (6%)] | Loss: 0.424913 | Image2Text Acc: 87.50 | Text2Image Acc: 81.25 | Data Time: 0.024s | Batch Time: 0.366s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:18 | INFO | Rank 0 | Global Steps: 340/5215 | Train Epoch: 1 [16320/250320 (7%)] | Loss: 0.671518 | Image2Text Acc: 81.25 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.381s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:22 | INFO | Rank 0 | Global Steps: 350/5215 | Train Epoch: 1 [16800/250320 (7%)] | Loss: 0.533927 | Image2Text Acc: 79.17 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:25 | INFO | Rank 0 | Global Steps: 360/5215 | Train Epoch: 1 [17280/250320 (7%)] | Loss: 0.351673 | Image2Text Acc: 93.75 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:29 | INFO | Rank 0 | Global Steps: 370/5215 | Train Epoch: 1 [17760/250320 (7%)] | Loss: 0.423091 | Image2Text Acc: 87.50 | Text2Image Acc: 81.25 | Data Time: 0.021s | Batch Time: 0.370s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:33 | INFO | Rank 0 | Global Steps: 380/5215 | Train Epoch: 1 [18240/250320 (7%)] | Loss: 0.391531 | Image2Text Acc: 83.33 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:37 | INFO | Rank 0 | Global Steps: 390/5215 | Train Epoch: 1 [18720/250320 (7%)] | Loss: 0.566264 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.024s | Batch Time: 0.380s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:40 | INFO | Rank 0 | Global Steps: 400/5215 | Train Epoch: 1 [19200/250320 (8%)] | Loss: 0.520908 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.024s | Batch Time: 0.370s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:44 | INFO | Rank 0 | Global Steps: 410/5215 | Train Epoch: 1 [19680/250320 (8%)] | Loss: 0.530346 | Image2Text Acc: 85.42 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:48 | INFO | Rank 0 | Global Steps: 420/5215 | Train Epoch: 1 [20160/250320 (8%)] | Loss: 0.168560 | Image2Text Acc: 91.67 | Text2Image Acc: 100.00 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:51 | INFO | Rank 0 | Global Steps: 430/5215 | Train Epoch: 1 [20640/250320 (8%)] | Loss: 0.365425 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:55 | INFO | Rank 0 | Global Steps: 440/5215 | Train Epoch: 1 [21120/250320 (8%)] | Loss: 0.561810 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:34:58 | INFO | Rank 0 | Global Steps: 450/5215 | Train Epoch: 1 [21600/250320 (9%)] | Loss: 0.176170 | Image2Text Acc: 91.67 | Text2Image Acc: 95.83 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:02 | INFO | Rank 0 | Global Steps: 460/5215 | Train Epoch: 1 [22080/250320 (9%)] | Loss: 0.339763 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:06 | INFO | Rank 0 | Global Steps: 470/5215 | Train Epoch: 1 [22560/250320 (9%)] | Loss: 0.495715 | Image2Text Acc: 89.58 | Text2Image Acc: 79.17 | Data Time: 0.023s | Batch Time: 0.365s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:09 | INFO | Rank 0 | Global Steps: 480/5215 | Train Epoch: 1 [23040/250320 (9%)] | Loss: 0.337739 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:13 | INFO | Rank 0 | Global Steps: 490/5215 | Train Epoch: 1 [23520/250320 (9%)] | Loss: 0.657292 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:17 | INFO | Rank 0 | Global Steps: 500/5215 | Train Epoch: 1 [24000/250320 (10%)] | Loss: 0.432810 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:20 | INFO | Rank 0 | Global Steps: 510/5215 | Train Epoch: 1 [24480/250320 (10%)] | Loss: 0.441927 | Image2Text Acc: 83.33 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:24 | INFO | Rank 0 | Global Steps: 520/5215 | Train Epoch: 1 [24960/250320 (10%)] | Loss: 0.298030 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.018s | Batch Time: 0.375s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:28 | INFO | Rank 0 | Global Steps: 530/5215 | Train Epoch: 1 [25440/250320 (10%)] | Loss: 0.484386 | Image2Text Acc: 83.33 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:31 | INFO | Rank 0 | Global Steps: 540/5215 | Train Epoch: 1 [25920/250320 (10%)] | Loss: 0.637108 | Image2Text Acc: 81.25 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:35 | INFO | Rank 0 | Global Steps: 550/5215 | Train Epoch: 1 [26400/250320 (11%)] | Loss: 0.214095 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:39 | INFO | Rank 0 | Global Steps: 560/5215 | Train Epoch: 1 [26880/250320 (11%)] | Loss: 0.358855 | Image2Text Acc: 87.50 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:42 | INFO | Rank 0 | Global Steps: 570/5215 | Train Epoch: 1 [27360/250320 (11%)] | Loss: 0.476662 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.021s | Batch Time: 0.368s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:46 | INFO | Rank 0 | Global Steps: 580/5215 | Train Epoch: 1 [27840/250320 (11%)] | Loss: 0.705014 | Image2Text Acc: 72.92 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.369s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:50 | INFO | Rank 0 | Global Steps: 590/5215 | Train Epoch: 1 [28320/250320 (11%)] | Loss: 0.389719 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.024s | Batch Time: 0.368s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:53 | INFO | Rank 0 | Global Steps: 600/5215 | Train Epoch: 1 [28800/250320 (12%)] | Loss: 0.567398 | Image2Text Acc: 81.25 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:35:57 | INFO | Rank 0 | Global Steps: 610/5215 | Train Epoch: 1 [29280/250320 (12%)] | Loss: 0.268593 | Image2Text Acc: 97.92 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.371s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:01 | INFO | Rank 0 | Global Steps: 620/5215 | Train Epoch: 1 [29760/250320 (12%)] | Loss: 0.450645 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.366s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:04 | INFO | Rank 0 | Global Steps: 630/5215 | Train Epoch: 1 [30240/250320 (12%)] | Loss: 0.451358 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:08 | INFO | Rank 0 | Global Steps: 640/5215 | Train Epoch: 1 [30720/250320 (12%)] | Loss: 0.474468 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.370s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:12 | INFO | Rank 0 | Global Steps: 650/5215 | Train Epoch: 1 [31200/250320 (12%)] | Loss: 0.540700 | Image2Text Acc: 83.33 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.394s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:16 | INFO | Rank 0 | Global Steps: 660/5215 | Train Epoch: 1 [31680/250320 (13%)] | Loss: 0.520677 | Image2Text Acc: 79.17 | Text2Image Acc: 79.17 | Data Time: 0.020s | Batch Time: 0.366s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:19 | INFO | Rank 0 | Global Steps: 670/5215 | Train Epoch: 1 [32160/250320 (13%)] | Loss: 0.156446 | Image2Text Acc: 97.92 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.366s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:23 | INFO | Rank 0 | Global Steps: 680/5215 | Train Epoch: 1 [32640/250320 (13%)] | Loss: 0.233609 | Image2Text Acc: 87.50 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:26 | INFO | Rank 0 | Global Steps: 690/5215 | Train Epoch: 1 [33120/250320 (13%)] | Loss: 0.453145 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.018s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:30 | INFO | Rank 0 | Global Steps: 700/5215 | Train Epoch: 1 [33600/250320 (13%)] | Loss: 0.429521 | Image2Text Acc: 83.33 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:34 | INFO | Rank 0 | Global Steps: 710/5215 | Train Epoch: 1 [34080/250320 (14%)] | Loss: 0.699030 | Image2Text Acc: 81.25 | Text2Image Acc: 77.08 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:37 | INFO | Rank 0 | Global Steps: 720/5215 | Train Epoch: 1 [34560/250320 (14%)] | Loss: 0.258114 | Image2Text Acc: 95.83 | Text2Image Acc: 91.67 | Data Time: 0.022s | Batch Time: 0.373s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:41 | INFO | Rank 0 | Global Steps: 730/5215 | Train Epoch: 1 [35040/250320 (14%)] | Loss: 0.348272 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.018s | Batch Time: 0.378s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:45 | INFO | Rank 0 | Global Steps: 740/5215 | Train Epoch: 1 [35520/250320 (14%)] | Loss: 0.487886 | Image2Text Acc: 81.25 | Text2Image Acc: 81.25 | Data Time: 0.021s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:48 | INFO | Rank 0 | Global Steps: 750/5215 | Train Epoch: 1 [36000/250320 (14%)] | Loss: 0.333101 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.022s | Batch Time: 0.373s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:52 | INFO | Rank 0 | Global Steps: 760/5215 | Train Epoch: 1 [36480/250320 (15%)] | Loss: 0.126650 | Image2Text Acc: 95.83 | Text2Image Acc: 95.83 | Data Time: 0.018s | Batch Time: 0.360s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:36:56 | INFO | Rank 0 | Global Steps: 770/5215 | Train Epoch: 1 [36960/250320 (15%)] | Loss: 0.556339 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.018s | Batch Time: 0.365s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:00 | INFO | Rank 0 | Global Steps: 780/5215 | Train Epoch: 1 [37440/250320 (15%)] | Loss: 0.462434 | Image2Text Acc: 87.50 | Text2Image Acc: 81.25 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:03 | INFO | Rank 0 | Global Steps: 790/5215 | Train Epoch: 1 [37920/250320 (15%)] | Loss: 0.285763 | Image2Text Acc: 89.58 | Text2Image Acc: 95.83 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:07 | INFO | Rank 0 | Global Steps: 800/5215 | Train Epoch: 1 [38400/250320 (15%)] | Loss: 0.447099 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:11 | INFO | Rank 0 | Global Steps: 810/5215 | Train Epoch: 1 [38880/250320 (16%)] | Loss: 0.679671 | Image2Text Acc: 81.25 | Text2Image Acc: 79.17 | Data Time: 0.021s | Batch Time: 0.372s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:14 | INFO | Rank 0 | Global Steps: 820/5215 | Train Epoch: 1 [39360/250320 (16%)] | Loss: 0.190679 | Image2Text Acc: 97.92 | Text2Image Acc: 91.67 | Data Time: 0.023s | Batch Time: 0.372s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:18 | INFO | Rank 0 | Global Steps: 830/5215 | Train Epoch: 1 [39840/250320 (16%)] | Loss: 0.241572 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.023s | Batch Time: 0.372s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:22 | INFO | Rank 0 | Global Steps: 840/5215 | Train Epoch: 1 [40320/250320 (16%)] | Loss: 0.771389 | Image2Text Acc: 77.08 | Text2Image Acc: 77.08 | Data Time: 0.022s | Batch Time: 0.376s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:25 | INFO | Rank 0 | Global Steps: 850/5215 | Train Epoch: 1 [40800/250320 (16%)] | Loss: 0.292248 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:29 | INFO | Rank 0 | Global Steps: 860/5215 | Train Epoch: 1 [41280/250320 (16%)] | Loss: 0.325587 | Image2Text Acc: 85.42 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:33 | INFO | Rank 0 | Global Steps: 870/5215 | Train Epoch: 1 [41760/250320 (17%)] | Loss: 0.588850 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:36 | INFO | Rank 0 | Global Steps: 880/5215 | Train Epoch: 1 [42240/250320 (17%)] | Loss: 0.308825 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:40 | INFO | Rank 0 | Global Steps: 890/5215 | Train Epoch: 1 [42720/250320 (17%)] | Loss: 0.427337 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.038s | Batch Time: 0.393s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:44 | INFO | Rank 0 | Global Steps: 900/5215 | Train Epoch: 1 [43200/250320 (17%)] | Loss: 0.320133 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:47 | INFO | Rank 0 | Global Steps: 910/5215 | Train Epoch: 1 [43680/250320 (17%)] | Loss: 0.621858 | Image2Text Acc: 81.25 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:51 | INFO | Rank 0 | Global Steps: 920/5215 | Train Epoch: 1 [44160/250320 (18%)] | Loss: 0.216431 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:55 | INFO | Rank 0 | Global Steps: 930/5215 | Train Epoch: 1 [44640/250320 (18%)] | Loss: 0.303966 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:37:58 | INFO | Rank 0 | Global Steps: 940/5215 | Train Epoch: 1 [45120/250320 (18%)] | Loss: 0.490032 | Image2Text Acc: 91.67 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:38:02 | INFO | Rank 0 | Global Steps: 950/5215 | Train Epoch: 1 [45600/250320 (18%)] | Loss: 0.768142 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:38:06 | INFO | Rank 0 | Global Steps: 960/5215 | Train Epoch: 1 [46080/250320 (18%)] | Loss: 0.185704 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:38:09 | INFO | Rank 0 | Global Steps: 970/5215 | Train Epoch: 1 [46560/250320 (19%)] | Loss: 0.631737 | Image2Text Acc: 81.25 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:38:13 | INFO | Rank 0 | Global Steps: 980/5215 | Train Epoch: 1 [47040/250320 (19%)] | Loss: 0.734770 | Image2Text Acc: 87.50 | Text2Image Acc: 77.08 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:38:17 | INFO | Rank 0 | Global Steps: 990/5215 | Train Epoch: 1 [47520/250320 (19%)] | Loss: 0.383750 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.370s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:38:20 | INFO | Rank 0 | Global Steps: 1000/5215 | Train Epoch: 1 [48000/250320 (19%)] | Loss: 0.277359 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.022s | Batch Time: 0.376s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:38:20 | INFO | Rank 0 | Begin to eval on validation set (epoch 1 @ 1000 steps)...\n",
      "/home/pai/lib/python3.6/site-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
      "2023-01-14,20:38:43 | INFO | Rank 0 | Evaluated 100/638 batches...\n",
      "2023-01-14,20:39:06 | INFO | Rank 0 | Evaluated 200/638 batches...\n",
      "2023-01-14,20:39:28 | INFO | Rank 0 | Evaluated 300/638 batches...\n",
      "2023-01-14,20:39:50 | INFO | Rank 0 | Evaluated 400/638 batches...\n",
      "2023-01-14,20:40:13 | INFO | Rank 0 | Evaluated 500/638 batches...\n",
      "2023-01-14,20:40:35 | INFO | Rank 0 | Evaluated 600/638 batches...\n",
      "2023-01-14,20:40:44 | INFO | Rank 0 | Validation Result (epoch 1 @ 1000 steps) | Valid Loss: 0.262804 | Image2Text Acc: 92.12 | Text2Image Acc: 91.45 | logit_scale: 4.605 | Valid Batch Size: 48\n",
      "2023-01-14,20:40:48 | INFO | Rank 0 | Global Steps: 1010/5215 | Train Epoch: 1 [48480/250320 (19%)] | Loss: 0.353489 | Image2Text Acc: 83.33 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:40:51 | INFO | Rank 0 | Global Steps: 1020/5215 | Train Epoch: 1 [48960/250320 (20%)] | Loss: 0.470332 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:40:55 | INFO | Rank 0 | Global Steps: 1030/5215 | Train Epoch: 1 [49440/250320 (20%)] | Loss: 0.329098 | Image2Text Acc: 89.58 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:40:59 | INFO | Rank 0 | Global Steps: 1040/5215 | Train Epoch: 1 [49920/250320 (20%)] | Loss: 0.525298 | Image2Text Acc: 83.33 | Text2Image Acc: 85.42 | Data Time: 0.039s | Batch Time: 0.396s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:02 | INFO | Rank 0 | Global Steps: 1050/5215 | Train Epoch: 1 [50400/250320 (20%)] | Loss: 0.293415 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.025s | Batch Time: 0.370s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:06 | INFO | Rank 0 | Global Steps: 1060/5215 | Train Epoch: 1 [50880/250320 (20%)] | Loss: 0.474844 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:10 | INFO | Rank 0 | Global Steps: 1070/5215 | Train Epoch: 1 [51360/250320 (21%)] | Loss: 0.498422 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:13 | INFO | Rank 0 | Global Steps: 1080/5215 | Train Epoch: 1 [51840/250320 (21%)] | Loss: 0.426774 | Image2Text Acc: 83.33 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:17 | INFO | Rank 0 | Global Steps: 1090/5215 | Train Epoch: 1 [52320/250320 (21%)] | Loss: 0.513126 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.021s | Batch Time: 0.371s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:21 | INFO | Rank 0 | Global Steps: 1100/5215 | Train Epoch: 1 [52800/250320 (21%)] | Loss: 0.645229 | Image2Text Acc: 89.58 | Text2Image Acc: 81.25 | Data Time: 0.022s | Batch Time: 0.372s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:24 | INFO | Rank 0 | Global Steps: 1110/5215 | Train Epoch: 1 [53280/250320 (21%)] | Loss: 0.357841 | Image2Text Acc: 91.67 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:28 | INFO | Rank 0 | Global Steps: 1120/5215 | Train Epoch: 1 [53760/250320 (21%)] | Loss: 0.467430 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:32 | INFO | Rank 0 | Global Steps: 1130/5215 | Train Epoch: 1 [54240/250320 (22%)] | Loss: 0.267649 | Image2Text Acc: 97.92 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.370s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:35 | INFO | Rank 0 | Global Steps: 1140/5215 | Train Epoch: 1 [54720/250320 (22%)] | Loss: 0.577349 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:39 | INFO | Rank 0 | Global Steps: 1150/5215 | Train Epoch: 1 [55200/250320 (22%)] | Loss: 0.521888 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.022s | Batch Time: 0.364s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:43 | INFO | Rank 0 | Global Steps: 1160/5215 | Train Epoch: 1 [55680/250320 (22%)] | Loss: 0.628832 | Image2Text Acc: 77.08 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:46 | INFO | Rank 0 | Global Steps: 1170/5215 | Train Epoch: 1 [56160/250320 (22%)] | Loss: 0.283388 | Image2Text Acc: 91.67 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:50 | INFO | Rank 0 | Global Steps: 1180/5215 | Train Epoch: 1 [56640/250320 (23%)] | Loss: 0.441064 | Image2Text Acc: 81.25 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:54 | INFO | Rank 0 | Global Steps: 1190/5215 | Train Epoch: 1 [57120/250320 (23%)] | Loss: 0.482407 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:41:57 | INFO | Rank 0 | Global Steps: 1200/5215 | Train Epoch: 1 [57600/250320 (23%)] | Loss: 0.190392 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.371s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:01 | INFO | Rank 0 | Global Steps: 1210/5215 | Train Epoch: 1 [58080/250320 (23%)] | Loss: 0.579661 | Image2Text Acc: 87.50 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:05 | INFO | Rank 0 | Global Steps: 1220/5215 | Train Epoch: 1 [58560/250320 (23%)] | Loss: 0.415587 | Image2Text Acc: 91.67 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:08 | INFO | Rank 0 | Global Steps: 1230/5215 | Train Epoch: 1 [59040/250320 (24%)] | Loss: 0.524482 | Image2Text Acc: 83.33 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:12 | INFO | Rank 0 | Global Steps: 1240/5215 | Train Epoch: 1 [59520/250320 (24%)] | Loss: 0.429102 | Image2Text Acc: 89.58 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:16 | INFO | Rank 0 | Global Steps: 1250/5215 | Train Epoch: 1 [60000/250320 (24%)] | Loss: 0.335256 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.372s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:19 | INFO | Rank 0 | Global Steps: 1260/5215 | Train Epoch: 1 [60480/250320 (24%)] | Loss: 0.168375 | Image2Text Acc: 97.92 | Text2Image Acc: 97.92 | Data Time: 0.018s | Batch Time: 0.374s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:23 | INFO | Rank 0 | Global Steps: 1270/5215 | Train Epoch: 1 [60960/250320 (24%)] | Loss: 0.427466 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:27 | INFO | Rank 0 | Global Steps: 1280/5215 | Train Epoch: 1 [61440/250320 (25%)] | Loss: 0.421137 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.022s | Batch Time: 0.372s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:30 | INFO | Rank 0 | Global Steps: 1290/5215 | Train Epoch: 1 [61920/250320 (25%)] | Loss: 0.284803 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:34 | INFO | Rank 0 | Global Steps: 1300/5215 | Train Epoch: 1 [62400/250320 (25%)] | Loss: 0.544901 | Image2Text Acc: 79.17 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.373s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:38 | INFO | Rank 0 | Global Steps: 1310/5215 | Train Epoch: 1 [62880/250320 (25%)] | Loss: 0.278784 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:41 | INFO | Rank 0 | Global Steps: 1320/5215 | Train Epoch: 1 [63360/250320 (25%)] | Loss: 0.307508 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:45 | INFO | Rank 0 | Global Steps: 1330/5215 | Train Epoch: 1 [63840/250320 (26%)] | Loss: 0.500920 | Image2Text Acc: 81.25 | Text2Image Acc: 87.50 | Data Time: 0.018s | Batch Time: 0.366s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:49 | INFO | Rank 0 | Global Steps: 1340/5215 | Train Epoch: 1 [64320/250320 (26%)] | Loss: 0.482300 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.018s | Batch Time: 0.364s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:52 | INFO | Rank 0 | Global Steps: 1350/5215 | Train Epoch: 1 [64800/250320 (26%)] | Loss: 0.310206 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:42:56 | INFO | Rank 0 | Global Steps: 1360/5215 | Train Epoch: 1 [65280/250320 (26%)] | Loss: 0.353309 | Image2Text Acc: 95.83 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.369s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:00 | INFO | Rank 0 | Global Steps: 1370/5215 | Train Epoch: 1 [65760/250320 (26%)] | Loss: 0.635376 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.022s | Batch Time: 0.381s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:03 | INFO | Rank 0 | Global Steps: 1380/5215 | Train Epoch: 1 [66240/250320 (26%)] | Loss: 0.552023 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:07 | INFO | Rank 0 | Global Steps: 1390/5215 | Train Epoch: 1 [66720/250320 (27%)] | Loss: 0.468239 | Image2Text Acc: 83.33 | Text2Image Acc: 89.58 | Data Time: 0.022s | Batch Time: 0.372s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:11 | INFO | Rank 0 | Global Steps: 1400/5215 | Train Epoch: 1 [67200/250320 (27%)] | Loss: 0.669208 | Image2Text Acc: 83.33 | Text2Image Acc: 77.08 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:14 | INFO | Rank 0 | Global Steps: 1410/5215 | Train Epoch: 1 [67680/250320 (27%)] | Loss: 0.338586 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:18 | INFO | Rank 0 | Global Steps: 1420/5215 | Train Epoch: 1 [68160/250320 (27%)] | Loss: 0.526634 | Image2Text Acc: 79.17 | Text2Image Acc: 81.25 | Data Time: 0.021s | Batch Time: 0.364s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:22 | INFO | Rank 0 | Global Steps: 1430/5215 | Train Epoch: 1 [68640/250320 (27%)] | Loss: 0.365810 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:25 | INFO | Rank 0 | Global Steps: 1440/5215 | Train Epoch: 1 [69120/250320 (28%)] | Loss: 0.573479 | Image2Text Acc: 79.17 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.368s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:29 | INFO | Rank 0 | Global Steps: 1450/5215 | Train Epoch: 1 [69600/250320 (28%)] | Loss: 0.635550 | Image2Text Acc: 89.58 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.377s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:33 | INFO | Rank 0 | Global Steps: 1460/5215 | Train Epoch: 1 [70080/250320 (28%)] | Loss: 0.609029 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.021s | Batch Time: 0.363s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:36 | INFO | Rank 0 | Global Steps: 1470/5215 | Train Epoch: 1 [70560/250320 (28%)] | Loss: 0.549076 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.017s | Batch Time: 0.360s | LR: 0.000003 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:40 | INFO | Rank 0 | Global Steps: 1480/5215 | Train Epoch: 1 [71040/250320 (28%)] | Loss: 0.413826 | Image2Text Acc: 83.33 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:44 | INFO | Rank 0 | Global Steps: 1490/5215 | Train Epoch: 1 [71520/250320 (29%)] | Loss: 0.297265 | Image2Text Acc: 93.75 | Text2Image Acc: 85.42 | Data Time: 0.022s | Batch Time: 0.372s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:47 | INFO | Rank 0 | Global Steps: 1500/5215 | Train Epoch: 1 [72000/250320 (29%)] | Loss: 0.553215 | Image2Text Acc: 87.50 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:51 | INFO | Rank 0 | Global Steps: 1510/5215 | Train Epoch: 1 [72480/250320 (29%)] | Loss: 0.610749 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:55 | INFO | Rank 0 | Global Steps: 1520/5215 | Train Epoch: 1 [72960/250320 (29%)] | Loss: 0.410754 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:43:58 | INFO | Rank 0 | Global Steps: 1530/5215 | Train Epoch: 1 [73440/250320 (29%)] | Loss: 0.491630 | Image2Text Acc: 81.25 | Text2Image Acc: 87.50 | Data Time: 0.026s | Batch Time: 0.368s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:02 | INFO | Rank 0 | Global Steps: 1540/5215 | Train Epoch: 1 [73920/250320 (30%)] | Loss: 0.182791 | Image2Text Acc: 97.92 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:06 | INFO | Rank 0 | Global Steps: 1550/5215 | Train Epoch: 1 [74400/250320 (30%)] | Loss: 0.398770 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.023s | Batch Time: 0.373s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:09 | INFO | Rank 0 | Global Steps: 1560/5215 | Train Epoch: 1 [74880/250320 (30%)] | Loss: 0.381658 | Image2Text Acc: 91.67 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:13 | INFO | Rank 0 | Global Steps: 1570/5215 | Train Epoch: 1 [75360/250320 (30%)] | Loss: 0.614655 | Image2Text Acc: 85.42 | Text2Image Acc: 81.25 | Data Time: 0.023s | Batch Time: 0.372s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:17 | INFO | Rank 0 | Global Steps: 1580/5215 | Train Epoch: 1 [75840/250320 (30%)] | Loss: 0.132453 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:20 | INFO | Rank 0 | Global Steps: 1590/5215 | Train Epoch: 1 [76320/250320 (30%)] | Loss: 0.488341 | Image2Text Acc: 81.25 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:24 | INFO | Rank 0 | Global Steps: 1600/5215 | Train Epoch: 1 [76800/250320 (31%)] | Loss: 0.377009 | Image2Text Acc: 85.42 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:28 | INFO | Rank 0 | Global Steps: 1610/5215 | Train Epoch: 1 [77280/250320 (31%)] | Loss: 0.183717 | Image2Text Acc: 95.83 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:31 | INFO | Rank 0 | Global Steps: 1620/5215 | Train Epoch: 1 [77760/250320 (31%)] | Loss: 0.456591 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.370s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:35 | INFO | Rank 0 | Global Steps: 1630/5215 | Train Epoch: 1 [78240/250320 (31%)] | Loss: 0.476531 | Image2Text Acc: 83.33 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:39 | INFO | Rank 0 | Global Steps: 1640/5215 | Train Epoch: 1 [78720/250320 (31%)] | Loss: 0.452081 | Image2Text Acc: 79.17 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:42 | INFO | Rank 0 | Global Steps: 1650/5215 | Train Epoch: 1 [79200/250320 (32%)] | Loss: 0.321646 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.369s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:46 | INFO | Rank 0 | Global Steps: 1660/5215 | Train Epoch: 1 [79680/250320 (32%)] | Loss: 0.505794 | Image2Text Acc: 83.33 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:50 | INFO | Rank 0 | Global Steps: 1670/5215 | Train Epoch: 1 [80160/250320 (32%)] | Loss: 0.267335 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:53 | INFO | Rank 0 | Global Steps: 1680/5215 | Train Epoch: 1 [80640/250320 (32%)] | Loss: 0.444049 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:44:57 | INFO | Rank 0 | Global Steps: 1690/5215 | Train Epoch: 1 [81120/250320 (32%)] | Loss: 0.385360 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:01 | INFO | Rank 0 | Global Steps: 1700/5215 | Train Epoch: 1 [81600/250320 (33%)] | Loss: 0.587886 | Image2Text Acc: 85.42 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:04 | INFO | Rank 0 | Global Steps: 1710/5215 | Train Epoch: 1 [82080/250320 (33%)] | Loss: 0.512576 | Image2Text Acc: 87.50 | Text2Image Acc: 81.25 | Data Time: 0.022s | Batch Time: 0.373s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:08 | INFO | Rank 0 | Global Steps: 1720/5215 | Train Epoch: 1 [82560/250320 (33%)] | Loss: 0.295488 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.367s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:12 | INFO | Rank 0 | Global Steps: 1730/5215 | Train Epoch: 1 [83040/250320 (33%)] | Loss: 0.208858 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.366s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:15 | INFO | Rank 0 | Global Steps: 1740/5215 | Train Epoch: 1 [83520/250320 (33%)] | Loss: 0.335569 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:19 | INFO | Rank 0 | Global Steps: 1750/5215 | Train Epoch: 1 [84000/250320 (34%)] | Loss: 0.487836 | Image2Text Acc: 83.33 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:23 | INFO | Rank 0 | Global Steps: 1760/5215 | Train Epoch: 1 [84480/250320 (34%)] | Loss: 0.120877 | Image2Text Acc: 93.75 | Text2Image Acc: 95.83 | Data Time: 0.023s | Batch Time: 0.365s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:26 | INFO | Rank 0 | Global Steps: 1770/5215 | Train Epoch: 1 [84960/250320 (34%)] | Loss: 0.314583 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:30 | INFO | Rank 0 | Global Steps: 1780/5215 | Train Epoch: 1 [85440/250320 (34%)] | Loss: 0.542514 | Image2Text Acc: 87.50 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:33 | INFO | Rank 0 | Global Steps: 1790/5215 | Train Epoch: 1 [85920/250320 (34%)] | Loss: 0.249195 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:37 | INFO | Rank 0 | Global Steps: 1800/5215 | Train Epoch: 1 [86400/250320 (35%)] | Loss: 0.240446 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.376s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:41 | INFO | Rank 0 | Global Steps: 1810/5215 | Train Epoch: 1 [86880/250320 (35%)] | Loss: 0.368746 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:44 | INFO | Rank 0 | Global Steps: 1820/5215 | Train Epoch: 1 [87360/250320 (35%)] | Loss: 0.278901 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.022s | Batch Time: 0.378s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:48 | INFO | Rank 0 | Global Steps: 1830/5215 | Train Epoch: 1 [87840/250320 (35%)] | Loss: 0.276916 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:52 | INFO | Rank 0 | Global Steps: 1840/5215 | Train Epoch: 1 [88320/250320 (35%)] | Loss: 0.216239 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:55 | INFO | Rank 0 | Global Steps: 1850/5215 | Train Epoch: 1 [88800/250320 (35%)] | Loss: 0.540644 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:45:59 | INFO | Rank 0 | Global Steps: 1860/5215 | Train Epoch: 1 [89280/250320 (36%)] | Loss: 0.429113 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:03 | INFO | Rank 0 | Global Steps: 1870/5215 | Train Epoch: 1 [89760/250320 (36%)] | Loss: 0.251663 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:06 | INFO | Rank 0 | Global Steps: 1880/5215 | Train Epoch: 1 [90240/250320 (36%)] | Loss: 0.526177 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:10 | INFO | Rank 0 | Global Steps: 1890/5215 | Train Epoch: 1 [90720/250320 (36%)] | Loss: 0.227536 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:14 | INFO | Rank 0 | Global Steps: 1900/5215 | Train Epoch: 1 [91200/250320 (36%)] | Loss: 0.671569 | Image2Text Acc: 77.08 | Text2Image Acc: 81.25 | Data Time: 0.018s | Batch Time: 0.364s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:17 | INFO | Rank 0 | Global Steps: 1910/5215 | Train Epoch: 1 [91680/250320 (37%)] | Loss: 0.428948 | Image2Text Acc: 81.25 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:21 | INFO | Rank 0 | Global Steps: 1920/5215 | Train Epoch: 1 [92160/250320 (37%)] | Loss: 0.332258 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:24 | INFO | Rank 0 | Global Steps: 1930/5215 | Train Epoch: 1 [92640/250320 (37%)] | Loss: 0.359012 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.376s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:28 | INFO | Rank 0 | Global Steps: 1940/5215 | Train Epoch: 1 [93120/250320 (37%)] | Loss: 0.802764 | Image2Text Acc: 77.08 | Text2Image Acc: 75.00 | Data Time: 0.022s | Batch Time: 0.373s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:32 | INFO | Rank 0 | Global Steps: 1950/5215 | Train Epoch: 1 [93600/250320 (37%)] | Loss: 0.622857 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:35 | INFO | Rank 0 | Global Steps: 1960/5215 | Train Epoch: 1 [94080/250320 (38%)] | Loss: 0.215016 | Image2Text Acc: 95.83 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:39 | INFO | Rank 0 | Global Steps: 1970/5215 | Train Epoch: 1 [94560/250320 (38%)] | Loss: 0.365139 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:43 | INFO | Rank 0 | Global Steps: 1980/5215 | Train Epoch: 1 [95040/250320 (38%)] | Loss: 0.214285 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.021s | Batch Time: 0.370s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:47 | INFO | Rank 0 | Global Steps: 1990/5215 | Train Epoch: 1 [95520/250320 (38%)] | Loss: 0.288544 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:50 | INFO | Rank 0 | Global Steps: 2000/5215 | Train Epoch: 1 [96000/250320 (38%)] | Loss: 0.508433 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:46:50 | INFO | Rank 0 | Begin to eval on validation set (epoch 1 @ 2000 steps)...\n",
      "2023-01-14,20:47:13 | INFO | Rank 0 | Evaluated 100/638 batches...\n",
      "2023-01-14,20:47:36 | INFO | Rank 0 | Evaluated 200/638 batches...\n",
      "2023-01-14,20:47:58 | INFO | Rank 0 | Evaluated 300/638 batches...\n",
      "2023-01-14,20:48:21 | INFO | Rank 0 | Evaluated 400/638 batches...\n",
      "2023-01-14,20:48:43 | INFO | Rank 0 | Evaluated 500/638 batches...\n",
      "2023-01-14,20:49:05 | INFO | Rank 0 | Evaluated 600/638 batches...\n",
      "2023-01-14,20:49:14 | INFO | Rank 0 | Validation Result (epoch 1 @ 2000 steps) | Valid Loss: 0.253930 | Image2Text Acc: 92.30 | Text2Image Acc: 91.60 | logit_scale: 4.605 | Valid Batch Size: 48\n",
      "2023-01-14,20:49:18 | INFO | Rank 0 | Global Steps: 2010/5215 | Train Epoch: 1 [96480/250320 (39%)] | Loss: 0.393346 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:22 | INFO | Rank 0 | Global Steps: 2020/5215 | Train Epoch: 1 [96960/250320 (39%)] | Loss: 0.236084 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.022s | Batch Time: 0.373s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:25 | INFO | Rank 0 | Global Steps: 2030/5215 | Train Epoch: 1 [97440/250320 (39%)] | Loss: 0.247176 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:29 | INFO | Rank 0 | Global Steps: 2040/5215 | Train Epoch: 1 [97920/250320 (39%)] | Loss: 0.489473 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.023s | Batch Time: 0.366s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:33 | INFO | Rank 0 | Global Steps: 2050/5215 | Train Epoch: 1 [98400/250320 (39%)] | Loss: 0.177373 | Image2Text Acc: 93.75 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:37 | INFO | Rank 0 | Global Steps: 2060/5215 | Train Epoch: 1 [98880/250320 (40%)] | Loss: 0.446684 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:40 | INFO | Rank 0 | Global Steps: 2070/5215 | Train Epoch: 1 [99360/250320 (40%)] | Loss: 0.361537 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:44 | INFO | Rank 0 | Global Steps: 2080/5215 | Train Epoch: 1 [99840/250320 (40%)] | Loss: 0.532427 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:47 | INFO | Rank 0 | Global Steps: 2090/5215 | Train Epoch: 1 [100320/250320 (40%)] | Loss: 0.338953 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:51 | INFO | Rank 0 | Global Steps: 2100/5215 | Train Epoch: 1 [100800/250320 (40%)] | Loss: 0.324819 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:55 | INFO | Rank 0 | Global Steps: 2110/5215 | Train Epoch: 1 [101280/250320 (40%)] | Loss: 0.305668 | Image2Text Acc: 95.83 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:49:58 | INFO | Rank 0 | Global Steps: 2120/5215 | Train Epoch: 1 [101760/250320 (41%)] | Loss: 0.428335 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:02 | INFO | Rank 0 | Global Steps: 2130/5215 | Train Epoch: 1 [102240/250320 (41%)] | Loss: 0.235824 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.025s | Batch Time: 0.381s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:06 | INFO | Rank 0 | Global Steps: 2140/5215 | Train Epoch: 1 [102720/250320 (41%)] | Loss: 0.396628 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:09 | INFO | Rank 0 | Global Steps: 2150/5215 | Train Epoch: 1 [103200/250320 (41%)] | Loss: 0.435109 | Image2Text Acc: 83.33 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:13 | INFO | Rank 0 | Global Steps: 2160/5215 | Train Epoch: 1 [103680/250320 (41%)] | Loss: 0.549152 | Image2Text Acc: 87.50 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:17 | INFO | Rank 0 | Global Steps: 2170/5215 | Train Epoch: 1 [104160/250320 (42%)] | Loss: 0.462073 | Image2Text Acc: 91.67 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.376s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:20 | INFO | Rank 0 | Global Steps: 2180/5215 | Train Epoch: 1 [104640/250320 (42%)] | Loss: 0.337940 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.022s | Batch Time: 0.365s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:24 | INFO | Rank 0 | Global Steps: 2190/5215 | Train Epoch: 1 [105120/250320 (42%)] | Loss: 0.358022 | Image2Text Acc: 83.33 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.382s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:28 | INFO | Rank 0 | Global Steps: 2200/5215 | Train Epoch: 1 [105600/250320 (42%)] | Loss: 0.526386 | Image2Text Acc: 79.17 | Text2Image Acc: 83.33 | Data Time: 0.022s | Batch Time: 0.364s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:32 | INFO | Rank 0 | Global Steps: 2210/5215 | Train Epoch: 1 [106080/250320 (42%)] | Loss: 0.232042 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.380s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:35 | INFO | Rank 0 | Global Steps: 2220/5215 | Train Epoch: 1 [106560/250320 (43%)] | Loss: 0.510376 | Image2Text Acc: 85.42 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:39 | INFO | Rank 0 | Global Steps: 2230/5215 | Train Epoch: 1 [107040/250320 (43%)] | Loss: 0.750720 | Image2Text Acc: 79.17 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:43 | INFO | Rank 0 | Global Steps: 2240/5215 | Train Epoch: 1 [107520/250320 (43%)] | Loss: 0.297693 | Image2Text Acc: 93.75 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:46 | INFO | Rank 0 | Global Steps: 2250/5215 | Train Epoch: 1 [108000/250320 (43%)] | Loss: 0.241137 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.366s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:50 | INFO | Rank 0 | Global Steps: 2260/5215 | Train Epoch: 1 [108480/250320 (43%)] | Loss: 0.691053 | Image2Text Acc: 79.17 | Text2Image Acc: 77.08 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:53 | INFO | Rank 0 | Global Steps: 2270/5215 | Train Epoch: 1 [108960/250320 (44%)] | Loss: 0.412174 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.021s | Batch Time: 0.367s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:50:57 | INFO | Rank 0 | Global Steps: 2280/5215 | Train Epoch: 1 [109440/250320 (44%)] | Loss: 0.310207 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:01 | INFO | Rank 0 | Global Steps: 2290/5215 | Train Epoch: 1 [109920/250320 (44%)] | Loss: 0.342477 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:04 | INFO | Rank 0 | Global Steps: 2300/5215 | Train Epoch: 1 [110400/250320 (44%)] | Loss: 0.565625 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:08 | INFO | Rank 0 | Global Steps: 2310/5215 | Train Epoch: 1 [110880/250320 (44%)] | Loss: 0.341602 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:12 | INFO | Rank 0 | Global Steps: 2320/5215 | Train Epoch: 1 [111360/250320 (44%)] | Loss: 0.529862 | Image2Text Acc: 85.42 | Text2Image Acc: 75.00 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:15 | INFO | Rank 0 | Global Steps: 2330/5215 | Train Epoch: 1 [111840/250320 (45%)] | Loss: 0.318515 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.374s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:19 | INFO | Rank 0 | Global Steps: 2340/5215 | Train Epoch: 1 [112320/250320 (45%)] | Loss: 0.222913 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.025s | Batch Time: 0.367s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:23 | INFO | Rank 0 | Global Steps: 2350/5215 | Train Epoch: 1 [112800/250320 (45%)] | Loss: 0.673159 | Image2Text Acc: 79.17 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:27 | INFO | Rank 0 | Global Steps: 2360/5215 | Train Epoch: 1 [113280/250320 (45%)] | Loss: 0.820517 | Image2Text Acc: 77.08 | Text2Image Acc: 81.25 | Data Time: 0.023s | Batch Time: 0.377s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:30 | INFO | Rank 0 | Global Steps: 2370/5215 | Train Epoch: 1 [113760/250320 (45%)] | Loss: 0.331657 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.376s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:34 | INFO | Rank 0 | Global Steps: 2380/5215 | Train Epoch: 1 [114240/250320 (46%)] | Loss: 0.300309 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:37 | INFO | Rank 0 | Global Steps: 2390/5215 | Train Epoch: 1 [114720/250320 (46%)] | Loss: 0.251794 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:41 | INFO | Rank 0 | Global Steps: 2400/5215 | Train Epoch: 1 [115200/250320 (46%)] | Loss: 0.228814 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:45 | INFO | Rank 0 | Global Steps: 2410/5215 | Train Epoch: 1 [115680/250320 (46%)] | Loss: 0.579475 | Image2Text Acc: 85.42 | Text2Image Acc: 77.08 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:48 | INFO | Rank 0 | Global Steps: 2420/5215 | Train Epoch: 1 [116160/250320 (46%)] | Loss: 0.268366 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.018s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:52 | INFO | Rank 0 | Global Steps: 2430/5215 | Train Epoch: 1 [116640/250320 (47%)] | Loss: 0.392079 | Image2Text Acc: 91.67 | Text2Image Acc: 87.50 | Data Time: 0.021s | Batch Time: 0.371s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:56 | INFO | Rank 0 | Global Steps: 2440/5215 | Train Epoch: 1 [117120/250320 (47%)] | Loss: 0.324616 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:51:59 | INFO | Rank 0 | Global Steps: 2450/5215 | Train Epoch: 1 [117600/250320 (47%)] | Loss: 0.481112 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.364s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:03 | INFO | Rank 0 | Global Steps: 2460/5215 | Train Epoch: 1 [118080/250320 (47%)] | Loss: 0.431430 | Image2Text Acc: 87.50 | Text2Image Acc: 83.33 | Data Time: 0.018s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:07 | INFO | Rank 0 | Global Steps: 2470/5215 | Train Epoch: 1 [118560/250320 (47%)] | Loss: 0.092991 | Image2Text Acc: 97.92 | Text2Image Acc: 95.83 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:10 | INFO | Rank 0 | Global Steps: 2480/5215 | Train Epoch: 1 [119040/250320 (48%)] | Loss: 0.246772 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.022s | Batch Time: 0.371s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:14 | INFO | Rank 0 | Global Steps: 2490/5215 | Train Epoch: 1 [119520/250320 (48%)] | Loss: 0.489620 | Image2Text Acc: 83.33 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.364s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:18 | INFO | Rank 0 | Global Steps: 2500/5215 | Train Epoch: 1 [120000/250320 (48%)] | Loss: 0.644723 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:21 | INFO | Rank 0 | Global Steps: 2510/5215 | Train Epoch: 1 [120480/250320 (48%)] | Loss: 0.325605 | Image2Text Acc: 93.75 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:25 | INFO | Rank 0 | Global Steps: 2520/5215 | Train Epoch: 1 [120960/250320 (48%)] | Loss: 0.304364 | Image2Text Acc: 91.67 | Text2Image Acc: 87.50 | Data Time: 0.023s | Batch Time: 0.365s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:29 | INFO | Rank 0 | Global Steps: 2530/5215 | Train Epoch: 1 [121440/250320 (49%)] | Loss: 0.366834 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.386s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:33 | INFO | Rank 0 | Global Steps: 2540/5215 | Train Epoch: 1 [121920/250320 (49%)] | Loss: 0.383688 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.023s | Batch Time: 0.379s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:36 | INFO | Rank 0 | Global Steps: 2550/5215 | Train Epoch: 1 [122400/250320 (49%)] | Loss: 0.205350 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:40 | INFO | Rank 0 | Global Steps: 2560/5215 | Train Epoch: 1 [122880/250320 (49%)] | Loss: 0.536348 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:43 | INFO | Rank 0 | Global Steps: 2570/5215 | Train Epoch: 1 [123360/250320 (49%)] | Loss: 0.297162 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:47 | INFO | Rank 0 | Global Steps: 2580/5215 | Train Epoch: 1 [123840/250320 (49%)] | Loss: 0.297163 | Image2Text Acc: 93.75 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.372s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:51 | INFO | Rank 0 | Global Steps: 2590/5215 | Train Epoch: 1 [124320/250320 (50%)] | Loss: 0.352722 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.027s | Batch Time: 0.372s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:55 | INFO | Rank 0 | Global Steps: 2600/5215 | Train Epoch: 1 [124800/250320 (50%)] | Loss: 0.496191 | Image2Text Acc: 83.33 | Text2Image Acc: 75.00 | Data Time: 0.020s | Batch Time: 0.364s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:52:58 | INFO | Rank 0 | Global Steps: 2610/5215 | Train Epoch: 1 [125280/250320 (50%)] | Loss: 0.472608 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:02 | INFO | Rank 0 | Global Steps: 2620/5215 | Train Epoch: 1 [125760/250320 (50%)] | Loss: 0.373154 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.366s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:05 | INFO | Rank 0 | Global Steps: 2630/5215 | Train Epoch: 1 [126240/250320 (50%)] | Loss: 0.407097 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:09 | INFO | Rank 0 | Global Steps: 2640/5215 | Train Epoch: 1 [126720/250320 (51%)] | Loss: 0.259960 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:13 | INFO | Rank 0 | Global Steps: 2650/5215 | Train Epoch: 1 [127200/250320 (51%)] | Loss: 0.287041 | Image2Text Acc: 95.83 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.373s | LR: 0.000002 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:16 | INFO | Rank 0 | Global Steps: 2660/5215 | Train Epoch: 1 [127680/250320 (51%)] | Loss: 0.281949 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:20 | INFO | Rank 0 | Global Steps: 2670/5215 | Train Epoch: 1 [128160/250320 (51%)] | Loss: 0.483760 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.021s | Batch Time: 0.364s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:24 | INFO | Rank 0 | Global Steps: 2680/5215 | Train Epoch: 1 [128640/250320 (51%)] | Loss: 0.249935 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:27 | INFO | Rank 0 | Global Steps: 2690/5215 | Train Epoch: 1 [129120/250320 (52%)] | Loss: 0.228626 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:31 | INFO | Rank 0 | Global Steps: 2700/5215 | Train Epoch: 1 [129600/250320 (52%)] | Loss: 0.411693 | Image2Text Acc: 85.42 | Text2Image Acc: 89.58 | Data Time: 0.021s | Batch Time: 0.372s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:35 | INFO | Rank 0 | Global Steps: 2710/5215 | Train Epoch: 1 [130080/250320 (52%)] | Loss: 0.493524 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.022s | Batch Time: 0.371s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:38 | INFO | Rank 0 | Global Steps: 2720/5215 | Train Epoch: 1 [130560/250320 (52%)] | Loss: 0.219654 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:42 | INFO | Rank 0 | Global Steps: 2730/5215 | Train Epoch: 1 [131040/250320 (52%)] | Loss: 0.468917 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:46 | INFO | Rank 0 | Global Steps: 2740/5215 | Train Epoch: 1 [131520/250320 (53%)] | Loss: 0.251967 | Image2Text Acc: 91.67 | Text2Image Acc: 87.50 | Data Time: 0.018s | Batch Time: 0.360s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:49 | INFO | Rank 0 | Global Steps: 2750/5215 | Train Epoch: 1 [132000/250320 (53%)] | Loss: 0.437946 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.365s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:53 | INFO | Rank 0 | Global Steps: 2760/5215 | Train Epoch: 1 [132480/250320 (53%)] | Loss: 0.753715 | Image2Text Acc: 75.00 | Text2Image Acc: 77.08 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:53:57 | INFO | Rank 0 | Global Steps: 2770/5215 | Train Epoch: 1 [132960/250320 (53%)] | Loss: 0.520218 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.366s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:00 | INFO | Rank 0 | Global Steps: 2780/5215 | Train Epoch: 1 [133440/250320 (53%)] | Loss: 0.436514 | Image2Text Acc: 89.58 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.368s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:04 | INFO | Rank 0 | Global Steps: 2790/5215 | Train Epoch: 1 [133920/250320 (53%)] | Loss: 0.319248 | Image2Text Acc: 87.50 | Text2Image Acc: 93.75 | Data Time: 0.022s | Batch Time: 0.372s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:08 | INFO | Rank 0 | Global Steps: 2800/5215 | Train Epoch: 1 [134400/250320 (54%)] | Loss: 0.147926 | Image2Text Acc: 95.83 | Text2Image Acc: 95.83 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:11 | INFO | Rank 0 | Global Steps: 2810/5215 | Train Epoch: 1 [134880/250320 (54%)] | Loss: 0.329935 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:15 | INFO | Rank 0 | Global Steps: 2820/5215 | Train Epoch: 1 [135360/250320 (54%)] | Loss: 0.409039 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.018s | Batch Time: 0.375s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:19 | INFO | Rank 0 | Global Steps: 2830/5215 | Train Epoch: 1 [135840/250320 (54%)] | Loss: 0.349650 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.021s | Batch Time: 0.365s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:22 | INFO | Rank 0 | Global Steps: 2840/5215 | Train Epoch: 1 [136320/250320 (54%)] | Loss: 0.207253 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:26 | INFO | Rank 0 | Global Steps: 2850/5215 | Train Epoch: 1 [136800/250320 (55%)] | Loss: 0.643075 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:30 | INFO | Rank 0 | Global Steps: 2860/5215 | Train Epoch: 1 [137280/250320 (55%)] | Loss: 0.235392 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:33 | INFO | Rank 0 | Global Steps: 2870/5215 | Train Epoch: 1 [137760/250320 (55%)] | Loss: 0.228034 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:37 | INFO | Rank 0 | Global Steps: 2880/5215 | Train Epoch: 1 [138240/250320 (55%)] | Loss: 0.245669 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:41 | INFO | Rank 0 | Global Steps: 2890/5215 | Train Epoch: 1 [138720/250320 (55%)] | Loss: 0.166860 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:44 | INFO | Rank 0 | Global Steps: 2900/5215 | Train Epoch: 1 [139200/250320 (56%)] | Loss: 0.338099 | Image2Text Acc: 91.67 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:48 | INFO | Rank 0 | Global Steps: 2910/5215 | Train Epoch: 1 [139680/250320 (56%)] | Loss: 0.370300 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:52 | INFO | Rank 0 | Global Steps: 2920/5215 | Train Epoch: 1 [140160/250320 (56%)] | Loss: 0.400969 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:55 | INFO | Rank 0 | Global Steps: 2930/5215 | Train Epoch: 1 [140640/250320 (56%)] | Loss: 0.455307 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:54:59 | INFO | Rank 0 | Global Steps: 2940/5215 | Train Epoch: 1 [141120/250320 (56%)] | Loss: 0.533848 | Image2Text Acc: 87.50 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:55:03 | INFO | Rank 0 | Global Steps: 2950/5215 | Train Epoch: 1 [141600/250320 (57%)] | Loss: 0.435563 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.018s | Batch Time: 0.364s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:55:06 | INFO | Rank 0 | Global Steps: 2960/5215 | Train Epoch: 1 [142080/250320 (57%)] | Loss: 0.538444 | Image2Text Acc: 89.58 | Text2Image Acc: 83.33 | Data Time: 0.018s | Batch Time: 0.369s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:55:10 | INFO | Rank 0 | Global Steps: 2970/5215 | Train Epoch: 1 [142560/250320 (57%)] | Loss: 0.484170 | Image2Text Acc: 79.17 | Text2Image Acc: 83.33 | Data Time: 0.018s | Batch Time: 0.366s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:55:14 | INFO | Rank 0 | Global Steps: 2980/5215 | Train Epoch: 1 [143040/250320 (57%)] | Loss: 0.279289 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:55:17 | INFO | Rank 0 | Global Steps: 2990/5215 | Train Epoch: 1 [143520/250320 (57%)] | Loss: 0.269024 | Image2Text Acc: 85.42 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:55:21 | INFO | Rank 0 | Global Steps: 3000/5215 | Train Epoch: 1 [144000/250320 (58%)] | Loss: 0.299232 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.023s | Batch Time: 0.379s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:55:21 | INFO | Rank 0 | Begin to eval on validation set (epoch 1 @ 3000 steps)...\n",
      "2023-01-14,20:55:44 | INFO | Rank 0 | Evaluated 100/638 batches...\n",
      "2023-01-14,20:56:06 | INFO | Rank 0 | Evaluated 200/638 batches...\n",
      "2023-01-14,20:56:29 | INFO | Rank 0 | Evaluated 300/638 batches...\n",
      "2023-01-14,20:56:51 | INFO | Rank 0 | Evaluated 400/638 batches...\n",
      "2023-01-14,20:57:14 | INFO | Rank 0 | Evaluated 500/638 batches...\n",
      "2023-01-14,20:57:36 | INFO | Rank 0 | Evaluated 600/638 batches...\n",
      "2023-01-14,20:57:45 | INFO | Rank 0 | Validation Result (epoch 1 @ 3000 steps) | Valid Loss: 0.245162 | Image2Text Acc: 92.51 | Text2Image Acc: 91.86 | logit_scale: 4.605 | Valid Batch Size: 48\n",
      "2023-01-14,20:57:49 | INFO | Rank 0 | Global Steps: 3010/5215 | Train Epoch: 1 [144480/250320 (58%)] | Loss: 0.272546 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:57:52 | INFO | Rank 0 | Global Steps: 3020/5215 | Train Epoch: 1 [144960/250320 (58%)] | Loss: 0.447747 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.018s | Batch Time: 0.360s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:57:56 | INFO | Rank 0 | Global Steps: 3030/5215 | Train Epoch: 1 [145440/250320 (58%)] | Loss: 0.476081 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.021s | Batch Time: 0.376s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:00 | INFO | Rank 0 | Global Steps: 3040/5215 | Train Epoch: 1 [145920/250320 (58%)] | Loss: 0.267517 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:03 | INFO | Rank 0 | Global Steps: 3050/5215 | Train Epoch: 1 [146400/250320 (58%)] | Loss: 0.226274 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.372s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:07 | INFO | Rank 0 | Global Steps: 3060/5215 | Train Epoch: 1 [146880/250320 (59%)] | Loss: 0.217604 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:11 | INFO | Rank 0 | Global Steps: 3070/5215 | Train Epoch: 1 [147360/250320 (59%)] | Loss: 0.177885 | Image2Text Acc: 95.83 | Text2Image Acc: 91.67 | Data Time: 0.022s | Batch Time: 0.373s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:14 | INFO | Rank 0 | Global Steps: 3080/5215 | Train Epoch: 1 [147840/250320 (59%)] | Loss: 0.498376 | Image2Text Acc: 79.17 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:18 | INFO | Rank 0 | Global Steps: 3090/5215 | Train Epoch: 1 [148320/250320 (59%)] | Loss: 0.330423 | Image2Text Acc: 89.58 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:22 | INFO | Rank 0 | Global Steps: 3100/5215 | Train Epoch: 1 [148800/250320 (59%)] | Loss: 0.256121 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:25 | INFO | Rank 0 | Global Steps: 3110/5215 | Train Epoch: 1 [149280/250320 (60%)] | Loss: 0.385000 | Image2Text Acc: 93.75 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:29 | INFO | Rank 0 | Global Steps: 3120/5215 | Train Epoch: 1 [149760/250320 (60%)] | Loss: 0.430297 | Image2Text Acc: 93.75 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:33 | INFO | Rank 0 | Global Steps: 3130/5215 | Train Epoch: 1 [150240/250320 (60%)] | Loss: 0.522045 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.024s | Batch Time: 0.367s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:36 | INFO | Rank 0 | Global Steps: 3140/5215 | Train Epoch: 1 [150720/250320 (60%)] | Loss: 0.290708 | Image2Text Acc: 93.75 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.364s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:40 | INFO | Rank 0 | Global Steps: 3150/5215 | Train Epoch: 1 [151200/250320 (60%)] | Loss: 0.302918 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.022s | Batch Time: 0.373s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:44 | INFO | Rank 0 | Global Steps: 3160/5215 | Train Epoch: 1 [151680/250320 (61%)] | Loss: 0.187307 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.023s | Batch Time: 0.373s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:47 | INFO | Rank 0 | Global Steps: 3170/5215 | Train Epoch: 1 [152160/250320 (61%)] | Loss: 0.388520 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:51 | INFO | Rank 0 | Global Steps: 3180/5215 | Train Epoch: 1 [152640/250320 (61%)] | Loss: 0.156572 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.369s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:55 | INFO | Rank 0 | Global Steps: 3190/5215 | Train Epoch: 1 [153120/250320 (61%)] | Loss: 0.347776 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:58:58 | INFO | Rank 0 | Global Steps: 3200/5215 | Train Epoch: 1 [153600/250320 (61%)] | Loss: 0.381887 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:02 | INFO | Rank 0 | Global Steps: 3210/5215 | Train Epoch: 1 [154080/250320 (62%)] | Loss: 0.524301 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.371s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:06 | INFO | Rank 0 | Global Steps: 3220/5215 | Train Epoch: 1 [154560/250320 (62%)] | Loss: 0.297189 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.022s | Batch Time: 0.374s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:09 | INFO | Rank 0 | Global Steps: 3230/5215 | Train Epoch: 1 [155040/250320 (62%)] | Loss: 0.461027 | Image2Text Acc: 85.42 | Text2Image Acc: 81.25 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:13 | INFO | Rank 0 | Global Steps: 3240/5215 | Train Epoch: 1 [155520/250320 (62%)] | Loss: 0.436980 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:17 | INFO | Rank 0 | Global Steps: 3250/5215 | Train Epoch: 1 [156000/250320 (62%)] | Loss: 0.403678 | Image2Text Acc: 85.42 | Text2Image Acc: 81.25 | Data Time: 0.018s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:20 | INFO | Rank 0 | Global Steps: 3260/5215 | Train Epoch: 1 [156480/250320 (63%)] | Loss: 0.213571 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.021s | Batch Time: 0.375s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:24 | INFO | Rank 0 | Global Steps: 3270/5215 | Train Epoch: 1 [156960/250320 (63%)] | Loss: 0.356295 | Image2Text Acc: 87.50 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.364s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:28 | INFO | Rank 0 | Global Steps: 3280/5215 | Train Epoch: 1 [157440/250320 (63%)] | Loss: 0.166876 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:31 | INFO | Rank 0 | Global Steps: 3290/5215 | Train Epoch: 1 [157920/250320 (63%)] | Loss: 0.092873 | Image2Text Acc: 97.92 | Text2Image Acc: 97.92 | Data Time: 0.022s | Batch Time: 0.372s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:35 | INFO | Rank 0 | Global Steps: 3300/5215 | Train Epoch: 1 [158400/250320 (63%)] | Loss: 0.378582 | Image2Text Acc: 89.58 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:39 | INFO | Rank 0 | Global Steps: 3310/5215 | Train Epoch: 1 [158880/250320 (63%)] | Loss: 0.335712 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:42 | INFO | Rank 0 | Global Steps: 3320/5215 | Train Epoch: 1 [159360/250320 (64%)] | Loss: 0.232245 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:46 | INFO | Rank 0 | Global Steps: 3330/5215 | Train Epoch: 1 [159840/250320 (64%)] | Loss: 0.463921 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.367s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:50 | INFO | Rank 0 | Global Steps: 3340/5215 | Train Epoch: 1 [160320/250320 (64%)] | Loss: 0.489526 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:53 | INFO | Rank 0 | Global Steps: 3350/5215 | Train Epoch: 1 [160800/250320 (64%)] | Loss: 0.475601 | Image2Text Acc: 81.25 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,20:59:57 | INFO | Rank 0 | Global Steps: 3360/5215 | Train Epoch: 1 [161280/250320 (64%)] | Loss: 0.423765 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.369s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:01 | INFO | Rank 0 | Global Steps: 3370/5215 | Train Epoch: 1 [161760/250320 (65%)] | Loss: 0.205251 | Image2Text Acc: 87.50 | Text2Image Acc: 97.92 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:04 | INFO | Rank 0 | Global Steps: 3380/5215 | Train Epoch: 1 [162240/250320 (65%)] | Loss: 0.499233 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:08 | INFO | Rank 0 | Global Steps: 3390/5215 | Train Epoch: 1 [162720/250320 (65%)] | Loss: 0.453877 | Image2Text Acc: 83.33 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:12 | INFO | Rank 0 | Global Steps: 3400/5215 | Train Epoch: 1 [163200/250320 (65%)] | Loss: 0.205265 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:15 | INFO | Rank 0 | Global Steps: 3410/5215 | Train Epoch: 1 [163680/250320 (65%)] | Loss: 0.492467 | Image2Text Acc: 83.33 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.367s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:19 | INFO | Rank 0 | Global Steps: 3420/5215 | Train Epoch: 1 [164160/250320 (66%)] | Loss: 0.634184 | Image2Text Acc: 85.42 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.370s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:23 | INFO | Rank 0 | Global Steps: 3430/5215 | Train Epoch: 1 [164640/250320 (66%)] | Loss: 0.384186 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:26 | INFO | Rank 0 | Global Steps: 3440/5215 | Train Epoch: 1 [165120/250320 (66%)] | Loss: 0.489579 | Image2Text Acc: 87.50 | Text2Image Acc: 77.08 | Data Time: 0.023s | Batch Time: 0.365s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:30 | INFO | Rank 0 | Global Steps: 3450/5215 | Train Epoch: 1 [165600/250320 (66%)] | Loss: 0.299198 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:34 | INFO | Rank 0 | Global Steps: 3460/5215 | Train Epoch: 1 [166080/250320 (66%)] | Loss: 0.154600 | Image2Text Acc: 95.83 | Text2Image Acc: 97.92 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:37 | INFO | Rank 0 | Global Steps: 3470/5215 | Train Epoch: 1 [166560/250320 (67%)] | Loss: 0.248392 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:41 | INFO | Rank 0 | Global Steps: 3480/5215 | Train Epoch: 1 [167040/250320 (67%)] | Loss: 0.321048 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:45 | INFO | Rank 0 | Global Steps: 3490/5215 | Train Epoch: 1 [167520/250320 (67%)] | Loss: 0.302907 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:48 | INFO | Rank 0 | Global Steps: 3500/5215 | Train Epoch: 1 [168000/250320 (67%)] | Loss: 0.296233 | Image2Text Acc: 95.83 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:52 | INFO | Rank 0 | Global Steps: 3510/5215 | Train Epoch: 1 [168480/250320 (67%)] | Loss: 0.310646 | Image2Text Acc: 91.67 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:56 | INFO | Rank 0 | Global Steps: 3520/5215 | Train Epoch: 1 [168960/250320 (67%)] | Loss: 0.258731 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:00:59 | INFO | Rank 0 | Global Steps: 3530/5215 | Train Epoch: 1 [169440/250320 (68%)] | Loss: 0.422833 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:03 | INFO | Rank 0 | Global Steps: 3540/5215 | Train Epoch: 1 [169920/250320 (68%)] | Loss: 0.416619 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:07 | INFO | Rank 0 | Global Steps: 3550/5215 | Train Epoch: 1 [170400/250320 (68%)] | Loss: 0.758026 | Image2Text Acc: 75.00 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:10 | INFO | Rank 0 | Global Steps: 3560/5215 | Train Epoch: 1 [170880/250320 (68%)] | Loss: 0.379954 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.018s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:14 | INFO | Rank 0 | Global Steps: 3570/5215 | Train Epoch: 1 [171360/250320 (68%)] | Loss: 0.478845 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:17 | INFO | Rank 0 | Global Steps: 3580/5215 | Train Epoch: 1 [171840/250320 (69%)] | Loss: 0.339050 | Image2Text Acc: 87.50 | Text2Image Acc: 81.25 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:21 | INFO | Rank 0 | Global Steps: 3590/5215 | Train Epoch: 1 [172320/250320 (69%)] | Loss: 0.687952 | Image2Text Acc: 77.08 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:25 | INFO | Rank 0 | Global Steps: 3600/5215 | Train Epoch: 1 [172800/250320 (69%)] | Loss: 0.493248 | Image2Text Acc: 83.33 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:28 | INFO | Rank 0 | Global Steps: 3610/5215 | Train Epoch: 1 [173280/250320 (69%)] | Loss: 0.226441 | Image2Text Acc: 91.67 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:32 | INFO | Rank 0 | Global Steps: 3620/5215 | Train Epoch: 1 [173760/250320 (69%)] | Loss: 0.383224 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:36 | INFO | Rank 0 | Global Steps: 3630/5215 | Train Epoch: 1 [174240/250320 (70%)] | Loss: 0.515392 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:39 | INFO | Rank 0 | Global Steps: 3640/5215 | Train Epoch: 1 [174720/250320 (70%)] | Loss: 0.308256 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:43 | INFO | Rank 0 | Global Steps: 3650/5215 | Train Epoch: 1 [175200/250320 (70%)] | Loss: 0.703550 | Image2Text Acc: 79.17 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:46 | INFO | Rank 0 | Global Steps: 3660/5215 | Train Epoch: 1 [175680/250320 (70%)] | Loss: 0.371586 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:50 | INFO | Rank 0 | Global Steps: 3670/5215 | Train Epoch: 1 [176160/250320 (70%)] | Loss: 0.078936 | Image2Text Acc: 97.92 | Text2Image Acc: 97.92 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:54 | INFO | Rank 0 | Global Steps: 3680/5215 | Train Epoch: 1 [176640/250320 (71%)] | Loss: 0.286770 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:01:57 | INFO | Rank 0 | Global Steps: 3690/5215 | Train Epoch: 1 [177120/250320 (71%)] | Loss: 0.241136 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:01 | INFO | Rank 0 | Global Steps: 3700/5215 | Train Epoch: 1 [177600/250320 (71%)] | Loss: 0.342162 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:05 | INFO | Rank 0 | Global Steps: 3710/5215 | Train Epoch: 1 [178080/250320 (71%)] | Loss: 0.380985 | Image2Text Acc: 89.58 | Text2Image Acc: 83.33 | Data Time: 0.018s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:08 | INFO | Rank 0 | Global Steps: 3720/5215 | Train Epoch: 1 [178560/250320 (71%)] | Loss: 0.256423 | Image2Text Acc: 89.58 | Text2Image Acc: 93.75 | Data Time: 0.022s | Batch Time: 0.374s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:12 | INFO | Rank 0 | Global Steps: 3730/5215 | Train Epoch: 1 [179040/250320 (72%)] | Loss: 0.194401 | Image2Text Acc: 95.83 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:16 | INFO | Rank 0 | Global Steps: 3740/5215 | Train Epoch: 1 [179520/250320 (72%)] | Loss: 0.087899 | Image2Text Acc: 100.00 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.374s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:19 | INFO | Rank 0 | Global Steps: 3750/5215 | Train Epoch: 1 [180000/250320 (72%)] | Loss: 0.322438 | Image2Text Acc: 91.67 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:23 | INFO | Rank 0 | Global Steps: 3760/5215 | Train Epoch: 1 [180480/250320 (72%)] | Loss: 0.530988 | Image2Text Acc: 87.50 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:27 | INFO | Rank 0 | Global Steps: 3770/5215 | Train Epoch: 1 [180960/250320 (72%)] | Loss: 0.212841 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.380s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:30 | INFO | Rank 0 | Global Steps: 3780/5215 | Train Epoch: 1 [181440/250320 (72%)] | Loss: 0.546179 | Image2Text Acc: 91.67 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:34 | INFO | Rank 0 | Global Steps: 3790/5215 | Train Epoch: 1 [181920/250320 (73%)] | Loss: 0.480567 | Image2Text Acc: 81.25 | Text2Image Acc: 81.25 | Data Time: 0.022s | Batch Time: 0.371s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:38 | INFO | Rank 0 | Global Steps: 3800/5215 | Train Epoch: 1 [182400/250320 (73%)] | Loss: 0.340113 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:41 | INFO | Rank 0 | Global Steps: 3810/5215 | Train Epoch: 1 [182880/250320 (73%)] | Loss: 0.432899 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:45 | INFO | Rank 0 | Global Steps: 3820/5215 | Train Epoch: 1 [183360/250320 (73%)] | Loss: 0.267409 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:49 | INFO | Rank 0 | Global Steps: 3830/5215 | Train Epoch: 1 [183840/250320 (73%)] | Loss: 0.552540 | Image2Text Acc: 83.33 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:52 | INFO | Rank 0 | Global Steps: 3840/5215 | Train Epoch: 1 [184320/250320 (74%)] | Loss: 0.200446 | Image2Text Acc: 95.83 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000001 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:56 | INFO | Rank 0 | Global Steps: 3850/5215 | Train Epoch: 1 [184800/250320 (74%)] | Loss: 0.244700 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:02:59 | INFO | Rank 0 | Global Steps: 3860/5215 | Train Epoch: 1 [185280/250320 (74%)] | Loss: 0.191211 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.022s | Batch Time: 0.382s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:03 | INFO | Rank 0 | Global Steps: 3870/5215 | Train Epoch: 1 [185760/250320 (74%)] | Loss: 0.579678 | Image2Text Acc: 91.67 | Text2Image Acc: 81.25 | Data Time: 0.020s | Batch Time: 0.365s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:07 | INFO | Rank 0 | Global Steps: 3880/5215 | Train Epoch: 1 [186240/250320 (74%)] | Loss: 0.406712 | Image2Text Acc: 93.75 | Text2Image Acc: 87.50 | Data Time: 0.022s | Batch Time: 0.374s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:11 | INFO | Rank 0 | Global Steps: 3890/5215 | Train Epoch: 1 [186720/250320 (75%)] | Loss: 0.292472 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.366s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:14 | INFO | Rank 0 | Global Steps: 3900/5215 | Train Epoch: 1 [187200/250320 (75%)] | Loss: 0.610871 | Image2Text Acc: 79.17 | Text2Image Acc: 79.17 | Data Time: 0.020s | Batch Time: 0.373s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:18 | INFO | Rank 0 | Global Steps: 3910/5215 | Train Epoch: 1 [187680/250320 (75%)] | Loss: 0.273239 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:22 | INFO | Rank 0 | Global Steps: 3920/5215 | Train Epoch: 1 [188160/250320 (75%)] | Loss: 0.226585 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:25 | INFO | Rank 0 | Global Steps: 3930/5215 | Train Epoch: 1 [188640/250320 (75%)] | Loss: 0.306814 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:29 | INFO | Rank 0 | Global Steps: 3940/5215 | Train Epoch: 1 [189120/250320 (76%)] | Loss: 0.193439 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:33 | INFO | Rank 0 | Global Steps: 3950/5215 | Train Epoch: 1 [189600/250320 (76%)] | Loss: 0.365571 | Image2Text Acc: 81.25 | Text2Image Acc: 91.67 | Data Time: 0.021s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:36 | INFO | Rank 0 | Global Steps: 3960/5215 | Train Epoch: 1 [190080/250320 (76%)] | Loss: 0.273761 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.022s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:40 | INFO | Rank 0 | Global Steps: 3970/5215 | Train Epoch: 1 [190560/250320 (76%)] | Loss: 0.455837 | Image2Text Acc: 81.25 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:44 | INFO | Rank 0 | Global Steps: 3980/5215 | Train Epoch: 1 [191040/250320 (76%)] | Loss: 0.410648 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:47 | INFO | Rank 0 | Global Steps: 3990/5215 | Train Epoch: 1 [191520/250320 (77%)] | Loss: 0.544485 | Image2Text Acc: 83.33 | Text2Image Acc: 85.42 | Data Time: 0.023s | Batch Time: 0.373s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:51 | INFO | Rank 0 | Global Steps: 4000/5215 | Train Epoch: 1 [192000/250320 (77%)] | Loss: 0.207225 | Image2Text Acc: 93.75 | Text2Image Acc: 95.83 | Data Time: 0.018s | Batch Time: 0.369s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:03:51 | INFO | Rank 0 | Begin to eval on validation set (epoch 1 @ 4000 steps)...\n",
      "2023-01-14,21:04:14 | INFO | Rank 0 | Evaluated 100/638 batches...\n",
      "2023-01-14,21:04:36 | INFO | Rank 0 | Evaluated 200/638 batches...\n",
      "2023-01-14,21:04:59 | INFO | Rank 0 | Evaluated 300/638 batches...\n",
      "2023-01-14,21:05:21 | INFO | Rank 0 | Evaluated 400/638 batches...\n",
      "2023-01-14,21:05:44 | INFO | Rank 0 | Evaluated 500/638 batches...\n",
      "2023-01-14,21:06:06 | INFO | Rank 0 | Evaluated 600/638 batches...\n",
      "2023-01-14,21:06:15 | INFO | Rank 0 | Validation Result (epoch 1 @ 4000 steps) | Valid Loss: 0.240804 | Image2Text Acc: 92.66 | Text2Image Acc: 91.94 | logit_scale: 4.605 | Valid Batch Size: 48\n",
      "2023-01-14,21:06:18 | INFO | Rank 0 | Global Steps: 4010/5215 | Train Epoch: 1 [192480/250320 (77%)] | Loss: 0.328553 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:22 | INFO | Rank 0 | Global Steps: 4020/5215 | Train Epoch: 1 [192960/250320 (77%)] | Loss: 0.152823 | Image2Text Acc: 95.83 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:26 | INFO | Rank 0 | Global Steps: 4030/5215 | Train Epoch: 1 [193440/250320 (77%)] | Loss: 0.260934 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.021s | Batch Time: 0.371s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:29 | INFO | Rank 0 | Global Steps: 4040/5215 | Train Epoch: 1 [193920/250320 (77%)] | Loss: 0.372008 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.022s | Batch Time: 0.366s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:33 | INFO | Rank 0 | Global Steps: 4050/5215 | Train Epoch: 1 [194400/250320 (78%)] | Loss: 0.442053 | Image2Text Acc: 91.67 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.366s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:37 | INFO | Rank 0 | Global Steps: 4060/5215 | Train Epoch: 1 [194880/250320 (78%)] | Loss: 0.370239 | Image2Text Acc: 83.33 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:40 | INFO | Rank 0 | Global Steps: 4070/5215 | Train Epoch: 1 [195360/250320 (78%)] | Loss: 0.259128 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.365s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:44 | INFO | Rank 0 | Global Steps: 4080/5215 | Train Epoch: 1 [195840/250320 (78%)] | Loss: 0.235972 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.021s | Batch Time: 0.379s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:48 | INFO | Rank 0 | Global Steps: 4090/5215 | Train Epoch: 1 [196320/250320 (78%)] | Loss: 0.232661 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:51 | INFO | Rank 0 | Global Steps: 4100/5215 | Train Epoch: 1 [196800/250320 (79%)] | Loss: 0.323244 | Image2Text Acc: 83.33 | Text2Image Acc: 87.50 | Data Time: 0.021s | Batch Time: 0.367s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:55 | INFO | Rank 0 | Global Steps: 4110/5215 | Train Epoch: 1 [197280/250320 (79%)] | Loss: 0.220095 | Image2Text Acc: 95.83 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:06:59 | INFO | Rank 0 | Global Steps: 4120/5215 | Train Epoch: 1 [197760/250320 (79%)] | Loss: 0.243769 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:02 | INFO | Rank 0 | Global Steps: 4130/5215 | Train Epoch: 1 [198240/250320 (79%)] | Loss: 0.458951 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.024s | Batch Time: 0.365s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:06 | INFO | Rank 0 | Global Steps: 4140/5215 | Train Epoch: 1 [198720/250320 (79%)] | Loss: 0.298845 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.021s | Batch Time: 0.375s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:10 | INFO | Rank 0 | Global Steps: 4150/5215 | Train Epoch: 1 [199200/250320 (80%)] | Loss: 0.467961 | Image2Text Acc: 91.67 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:13 | INFO | Rank 0 | Global Steps: 4160/5215 | Train Epoch: 1 [199680/250320 (80%)] | Loss: 0.436175 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.365s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:17 | INFO | Rank 0 | Global Steps: 4170/5215 | Train Epoch: 1 [200160/250320 (80%)] | Loss: 0.262451 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:21 | INFO | Rank 0 | Global Steps: 4180/5215 | Train Epoch: 1 [200640/250320 (80%)] | Loss: 0.545027 | Image2Text Acc: 85.42 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:24 | INFO | Rank 0 | Global Steps: 4190/5215 | Train Epoch: 1 [201120/250320 (80%)] | Loss: 0.564339 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:28 | INFO | Rank 0 | Global Steps: 4200/5215 | Train Epoch: 1 [201600/250320 (81%)] | Loss: 0.627127 | Image2Text Acc: 83.33 | Text2Image Acc: 81.25 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:32 | INFO | Rank 0 | Global Steps: 4210/5215 | Train Epoch: 1 [202080/250320 (81%)] | Loss: 0.324819 | Image2Text Acc: 95.83 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:35 | INFO | Rank 0 | Global Steps: 4220/5215 | Train Epoch: 1 [202560/250320 (81%)] | Loss: 0.407902 | Image2Text Acc: 83.33 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:39 | INFO | Rank 0 | Global Steps: 4230/5215 | Train Epoch: 1 [203040/250320 (81%)] | Loss: 0.320982 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.023s | Batch Time: 0.392s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:43 | INFO | Rank 0 | Global Steps: 4240/5215 | Train Epoch: 1 [203520/250320 (81%)] | Loss: 0.484319 | Image2Text Acc: 85.42 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.368s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:46 | INFO | Rank 0 | Global Steps: 4250/5215 | Train Epoch: 1 [204000/250320 (81%)] | Loss: 0.147294 | Image2Text Acc: 95.83 | Text2Image Acc: 100.00 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:50 | INFO | Rank 0 | Global Steps: 4260/5215 | Train Epoch: 1 [204480/250320 (82%)] | Loss: 0.169659 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.375s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:54 | INFO | Rank 0 | Global Steps: 4270/5215 | Train Epoch: 1 [204960/250320 (82%)] | Loss: 0.272106 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.023s | Batch Time: 0.377s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:07:57 | INFO | Rank 0 | Global Steps: 4280/5215 | Train Epoch: 1 [205440/250320 (82%)] | Loss: 0.326259 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:01 | INFO | Rank 0 | Global Steps: 4290/5215 | Train Epoch: 1 [205920/250320 (82%)] | Loss: 0.337684 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:05 | INFO | Rank 0 | Global Steps: 4300/5215 | Train Epoch: 1 [206400/250320 (82%)] | Loss: 0.381485 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:08 | INFO | Rank 0 | Global Steps: 4310/5215 | Train Epoch: 1 [206880/250320 (83%)] | Loss: 0.256099 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:12 | INFO | Rank 0 | Global Steps: 4320/5215 | Train Epoch: 1 [207360/250320 (83%)] | Loss: 0.373579 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:16 | INFO | Rank 0 | Global Steps: 4330/5215 | Train Epoch: 1 [207840/250320 (83%)] | Loss: 0.245633 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:19 | INFO | Rank 0 | Global Steps: 4340/5215 | Train Epoch: 1 [208320/250320 (83%)] | Loss: 0.499349 | Image2Text Acc: 83.33 | Text2Image Acc: 85.42 | Data Time: 0.026s | Batch Time: 0.368s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:23 | INFO | Rank 0 | Global Steps: 4350/5215 | Train Epoch: 1 [208800/250320 (83%)] | Loss: 0.190400 | Image2Text Acc: 93.75 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:27 | INFO | Rank 0 | Global Steps: 4360/5215 | Train Epoch: 1 [209280/250320 (84%)] | Loss: 0.299177 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:30 | INFO | Rank 0 | Global Steps: 4370/5215 | Train Epoch: 1 [209760/250320 (84%)] | Loss: 0.320892 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:34 | INFO | Rank 0 | Global Steps: 4380/5215 | Train Epoch: 1 [210240/250320 (84%)] | Loss: 0.260111 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:38 | INFO | Rank 0 | Global Steps: 4390/5215 | Train Epoch: 1 [210720/250320 (84%)] | Loss: 0.251131 | Image2Text Acc: 95.83 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:41 | INFO | Rank 0 | Global Steps: 4400/5215 | Train Epoch: 1 [211200/250320 (84%)] | Loss: 0.288052 | Image2Text Acc: 87.50 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:45 | INFO | Rank 0 | Global Steps: 4410/5215 | Train Epoch: 1 [211680/250320 (85%)] | Loss: 0.236768 | Image2Text Acc: 91.67 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:48 | INFO | Rank 0 | Global Steps: 4420/5215 | Train Epoch: 1 [212160/250320 (85%)] | Loss: 0.592548 | Image2Text Acc: 79.17 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:52 | INFO | Rank 0 | Global Steps: 4430/5215 | Train Epoch: 1 [212640/250320 (85%)] | Loss: 0.320176 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.367s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:08:56 | INFO | Rank 0 | Global Steps: 4440/5215 | Train Epoch: 1 [213120/250320 (85%)] | Loss: 0.205553 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.022s | Batch Time: 0.366s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:00 | INFO | Rank 0 | Global Steps: 4450/5215 | Train Epoch: 1 [213600/250320 (85%)] | Loss: 0.316974 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.030s | Batch Time: 0.372s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:03 | INFO | Rank 0 | Global Steps: 4460/5215 | Train Epoch: 1 [214080/250320 (86%)] | Loss: 0.160544 | Image2Text Acc: 93.75 | Text2Image Acc: 97.92 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:07 | INFO | Rank 0 | Global Steps: 4470/5215 | Train Epoch: 1 [214560/250320 (86%)] | Loss: 0.241178 | Image2Text Acc: 91.67 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:11 | INFO | Rank 0 | Global Steps: 4480/5215 | Train Epoch: 1 [215040/250320 (86%)] | Loss: 0.325726 | Image2Text Acc: 93.75 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.373s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:14 | INFO | Rank 0 | Global Steps: 4490/5215 | Train Epoch: 1 [215520/250320 (86%)] | Loss: 0.446931 | Image2Text Acc: 85.42 | Text2Image Acc: 87.50 | Data Time: 0.023s | Batch Time: 0.376s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:18 | INFO | Rank 0 | Global Steps: 4500/5215 | Train Epoch: 1 [216000/250320 (86%)] | Loss: 0.515944 | Image2Text Acc: 89.58 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:22 | INFO | Rank 0 | Global Steps: 4510/5215 | Train Epoch: 1 [216480/250320 (86%)] | Loss: 0.436900 | Image2Text Acc: 91.67 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:25 | INFO | Rank 0 | Global Steps: 4520/5215 | Train Epoch: 1 [216960/250320 (87%)] | Loss: 0.395753 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:29 | INFO | Rank 0 | Global Steps: 4530/5215 | Train Epoch: 1 [217440/250320 (87%)] | Loss: 0.298280 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.037s | Batch Time: 0.394s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:32 | INFO | Rank 0 | Global Steps: 4540/5215 | Train Epoch: 1 [217920/250320 (87%)] | Loss: 0.297875 | Image2Text Acc: 93.75 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:36 | INFO | Rank 0 | Global Steps: 4550/5215 | Train Epoch: 1 [218400/250320 (87%)] | Loss: 0.368746 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:40 | INFO | Rank 0 | Global Steps: 4560/5215 | Train Epoch: 1 [218880/250320 (87%)] | Loss: 0.299198 | Image2Text Acc: 91.67 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:43 | INFO | Rank 0 | Global Steps: 4570/5215 | Train Epoch: 1 [219360/250320 (88%)] | Loss: 0.391412 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.022s | Batch Time: 0.371s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:47 | INFO | Rank 0 | Global Steps: 4580/5215 | Train Epoch: 1 [219840/250320 (88%)] | Loss: 0.253173 | Image2Text Acc: 95.83 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:51 | INFO | Rank 0 | Global Steps: 4590/5215 | Train Epoch: 1 [220320/250320 (88%)] | Loss: 0.298226 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:54 | INFO | Rank 0 | Global Steps: 4600/5215 | Train Epoch: 1 [220800/250320 (88%)] | Loss: 0.505762 | Image2Text Acc: 81.25 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:09:58 | INFO | Rank 0 | Global Steps: 4610/5215 | Train Epoch: 1 [221280/250320 (88%)] | Loss: 0.368106 | Image2Text Acc: 83.33 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:02 | INFO | Rank 0 | Global Steps: 4620/5215 | Train Epoch: 1 [221760/250320 (89%)] | Loss: 0.099810 | Image2Text Acc: 95.83 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:05 | INFO | Rank 0 | Global Steps: 4630/5215 | Train Epoch: 1 [222240/250320 (89%)] | Loss: 0.271291 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:09 | INFO | Rank 0 | Global Steps: 4640/5215 | Train Epoch: 1 [222720/250320 (89%)] | Loss: 0.241306 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:13 | INFO | Rank 0 | Global Steps: 4650/5215 | Train Epoch: 1 [223200/250320 (89%)] | Loss: 0.393220 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:16 | INFO | Rank 0 | Global Steps: 4660/5215 | Train Epoch: 1 [223680/250320 (89%)] | Loss: 0.196638 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:20 | INFO | Rank 0 | Global Steps: 4670/5215 | Train Epoch: 1 [224160/250320 (90%)] | Loss: 0.207612 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:23 | INFO | Rank 0 | Global Steps: 4680/5215 | Train Epoch: 1 [224640/250320 (90%)] | Loss: 0.534915 | Image2Text Acc: 79.17 | Text2Image Acc: 83.33 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:27 | INFO | Rank 0 | Global Steps: 4690/5215 | Train Epoch: 1 [225120/250320 (90%)] | Loss: 0.335680 | Image2Text Acc: 95.83 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:31 | INFO | Rank 0 | Global Steps: 4700/5215 | Train Epoch: 1 [225600/250320 (90%)] | Loss: 0.379060 | Image2Text Acc: 83.33 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:34 | INFO | Rank 0 | Global Steps: 4710/5215 | Train Epoch: 1 [226080/250320 (90%)] | Loss: 0.195548 | Image2Text Acc: 91.67 | Text2Image Acc: 95.83 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:38 | INFO | Rank 0 | Global Steps: 4720/5215 | Train Epoch: 1 [226560/250320 (91%)] | Loss: 0.206264 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:42 | INFO | Rank 0 | Global Steps: 4730/5215 | Train Epoch: 1 [227040/250320 (91%)] | Loss: 0.344749 | Image2Text Acc: 89.58 | Text2Image Acc: 85.42 | Data Time: 0.020s | Batch Time: 0.370s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:45 | INFO | Rank 0 | Global Steps: 4740/5215 | Train Epoch: 1 [227520/250320 (91%)] | Loss: 0.401616 | Image2Text Acc: 93.75 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:49 | INFO | Rank 0 | Global Steps: 4750/5215 | Train Epoch: 1 [228000/250320 (91%)] | Loss: 0.193034 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.385s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:53 | INFO | Rank 0 | Global Steps: 4760/5215 | Train Epoch: 1 [228480/250320 (91%)] | Loss: 0.269970 | Image2Text Acc: 93.75 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:10:56 | INFO | Rank 0 | Global Steps: 4770/5215 | Train Epoch: 1 [228960/250320 (91%)] | Loss: 0.498633 | Image2Text Acc: 85.42 | Text2Image Acc: 81.25 | Data Time: 0.021s | Batch Time: 0.370s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:00 | INFO | Rank 0 | Global Steps: 4780/5215 | Train Epoch: 1 [229440/250320 (92%)] | Loss: 0.451746 | Image2Text Acc: 81.25 | Text2Image Acc: 85.42 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:04 | INFO | Rank 0 | Global Steps: 4790/5215 | Train Epoch: 1 [229920/250320 (92%)] | Loss: 0.455822 | Image2Text Acc: 81.25 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:07 | INFO | Rank 0 | Global Steps: 4800/5215 | Train Epoch: 1 [230400/250320 (92%)] | Loss: 0.270321 | Image2Text Acc: 91.67 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:11 | INFO | Rank 0 | Global Steps: 4810/5215 | Train Epoch: 1 [230880/250320 (92%)] | Loss: 0.382001 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:15 | INFO | Rank 0 | Global Steps: 4820/5215 | Train Epoch: 1 [231360/250320 (92%)] | Loss: 0.338040 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.021s | Batch Time: 0.370s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:18 | INFO | Rank 0 | Global Steps: 4830/5215 | Train Epoch: 1 [231840/250320 (93%)] | Loss: 0.218796 | Image2Text Acc: 89.58 | Text2Image Acc: 95.83 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:22 | INFO | Rank 0 | Global Steps: 4840/5215 | Train Epoch: 1 [232320/250320 (93%)] | Loss: 0.211760 | Image2Text Acc: 95.83 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:26 | INFO | Rank 0 | Global Steps: 4850/5215 | Train Epoch: 1 [232800/250320 (93%)] | Loss: 0.231990 | Image2Text Acc: 91.67 | Text2Image Acc: 95.83 | Data Time: 0.019s | Batch Time: 0.378s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:29 | INFO | Rank 0 | Global Steps: 4860/5215 | Train Epoch: 1 [233280/250320 (93%)] | Loss: 0.341754 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:33 | INFO | Rank 0 | Global Steps: 4870/5215 | Train Epoch: 1 [233760/250320 (93%)] | Loss: 0.143991 | Image2Text Acc: 93.75 | Text2Image Acc: 97.92 | Data Time: 0.018s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:37 | INFO | Rank 0 | Global Steps: 4880/5215 | Train Epoch: 1 [234240/250320 (94%)] | Loss: 0.280634 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:40 | INFO | Rank 0 | Global Steps: 4890/5215 | Train Epoch: 1 [234720/250320 (94%)] | Loss: 0.405270 | Image2Text Acc: 85.42 | Text2Image Acc: 93.75 | Data Time: 0.021s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:44 | INFO | Rank 0 | Global Steps: 4900/5215 | Train Epoch: 1 [235200/250320 (94%)] | Loss: 0.637904 | Image2Text Acc: 87.50 | Text2Image Acc: 79.17 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:48 | INFO | Rank 0 | Global Steps: 4910/5215 | Train Epoch: 1 [235680/250320 (94%)] | Loss: 0.244760 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:51 | INFO | Rank 0 | Global Steps: 4920/5215 | Train Epoch: 1 [236160/250320 (94%)] | Loss: 0.276766 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.365s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:55 | INFO | Rank 0 | Global Steps: 4930/5215 | Train Epoch: 1 [236640/250320 (95%)] | Loss: 0.377489 | Image2Text Acc: 89.58 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:11:59 | INFO | Rank 0 | Global Steps: 4940/5215 | Train Epoch: 1 [237120/250320 (95%)] | Loss: 0.329878 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.374s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:12:02 | INFO | Rank 0 | Global Steps: 4950/5215 | Train Epoch: 1 [237600/250320 (95%)] | Loss: 0.462497 | Image2Text Acc: 83.33 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:12:06 | INFO | Rank 0 | Global Steps: 4960/5215 | Train Epoch: 1 [238080/250320 (95%)] | Loss: 0.281811 | Image2Text Acc: 93.75 | Text2Image Acc: 85.42 | Data Time: 0.018s | Batch Time: 0.360s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:12:10 | INFO | Rank 0 | Global Steps: 4970/5215 | Train Epoch: 1 [238560/250320 (95%)] | Loss: 0.368849 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:12:13 | INFO | Rank 0 | Global Steps: 4980/5215 | Train Epoch: 1 [239040/250320 (95%)] | Loss: 0.242054 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.370s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:12:17 | INFO | Rank 0 | Global Steps: 4990/5215 | Train Epoch: 1 [239520/250320 (96%)] | Loss: 0.392416 | Image2Text Acc: 87.50 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:12:21 | INFO | Rank 0 | Global Steps: 5000/5215 | Train Epoch: 1 [240000/250320 (96%)] | Loss: 0.260949 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:12:21 | INFO | Rank 0 | Begin to eval on validation set (epoch 1 @ 5000 steps)...\n",
      "2023-01-14,21:12:44 | INFO | Rank 0 | Evaluated 100/638 batches...\n",
      "2023-01-14,21:13:06 | INFO | Rank 0 | Evaluated 200/638 batches...\n",
      "2023-01-14,21:13:28 | INFO | Rank 0 | Evaluated 300/638 batches...\n",
      "2023-01-14,21:13:51 | INFO | Rank 0 | Evaluated 400/638 batches...\n",
      "2023-01-14,21:14:13 | INFO | Rank 0 | Evaluated 500/638 batches...\n",
      "2023-01-14,21:14:36 | INFO | Rank 0 | Evaluated 600/638 batches...\n",
      "2023-01-14,21:14:44 | INFO | Rank 0 | Validation Result (epoch 1 @ 5000 steps) | Valid Loss: 0.238828 | Image2Text Acc: 92.71 | Text2Image Acc: 92.00 | logit_scale: 4.605 | Valid Batch Size: 48\n",
      "2023-01-14,21:14:48 | INFO | Rank 0 | Global Steps: 5010/5215 | Train Epoch: 1 [240480/250320 (96%)] | Loss: 0.296646 | Image2Text Acc: 91.67 | Text2Image Acc: 93.75 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:14:52 | INFO | Rank 0 | Global Steps: 5020/5215 | Train Epoch: 1 [240960/250320 (96%)] | Loss: 0.212507 | Image2Text Acc: 87.50 | Text2Image Acc: 93.75 | Data Time: 0.031s | Batch Time: 0.385s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:14:56 | INFO | Rank 0 | Global Steps: 5030/5215 | Train Epoch: 1 [241440/250320 (96%)] | Loss: 0.187542 | Image2Text Acc: 93.75 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:14:59 | INFO | Rank 0 | Global Steps: 5040/5215 | Train Epoch: 1 [241920/250320 (97%)] | Loss: 0.174362 | Image2Text Acc: 95.83 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:03 | INFO | Rank 0 | Global Steps: 5050/5215 | Train Epoch: 1 [242400/250320 (97%)] | Loss: 0.396933 | Image2Text Acc: 83.33 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:07 | INFO | Rank 0 | Global Steps: 5060/5215 | Train Epoch: 1 [242880/250320 (97%)] | Loss: 0.190303 | Image2Text Acc: 95.83 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:10 | INFO | Rank 0 | Global Steps: 5070/5215 | Train Epoch: 1 [243360/250320 (97%)] | Loss: 0.200660 | Image2Text Acc: 93.75 | Text2Image Acc: 95.83 | Data Time: 0.026s | Batch Time: 0.369s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:14 | INFO | Rank 0 | Global Steps: 5080/5215 | Train Epoch: 1 [243840/250320 (97%)] | Loss: 0.560694 | Image2Text Acc: 87.50 | Text2Image Acc: 83.33 | Data Time: 0.019s | Batch Time: 0.361s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:18 | INFO | Rank 0 | Global Steps: 5090/5215 | Train Epoch: 1 [244320/250320 (98%)] | Loss: 0.354665 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:21 | INFO | Rank 0 | Global Steps: 5100/5215 | Train Epoch: 1 [244800/250320 (98%)] | Loss: 0.173221 | Image2Text Acc: 97.92 | Text2Image Acc: 93.75 | Data Time: 0.019s | Batch Time: 0.364s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:25 | INFO | Rank 0 | Global Steps: 5110/5215 | Train Epoch: 1 [245280/250320 (98%)] | Loss: 0.559426 | Image2Text Acc: 81.25 | Text2Image Acc: 79.17 | Data Time: 0.021s | Batch Time: 0.371s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:28 | INFO | Rank 0 | Global Steps: 5120/5215 | Train Epoch: 1 [245760/250320 (98%)] | Loss: 0.356126 | Image2Text Acc: 89.58 | Text2Image Acc: 89.58 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:32 | INFO | Rank 0 | Global Steps: 5130/5215 | Train Epoch: 1 [246240/250320 (98%)] | Loss: 0.320622 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.022s | Batch Time: 0.364s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:36 | INFO | Rank 0 | Global Steps: 5140/5215 | Train Epoch: 1 [246720/250320 (99%)] | Loss: 0.293673 | Image2Text Acc: 91.67 | Text2Image Acc: 89.58 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:39 | INFO | Rank 0 | Global Steps: 5150/5215 | Train Epoch: 1 [247200/250320 (99%)] | Loss: 0.752282 | Image2Text Acc: 83.33 | Text2Image Acc: 81.25 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:43 | INFO | Rank 0 | Global Steps: 5160/5215 | Train Epoch: 1 [247680/250320 (99%)] | Loss: 0.363567 | Image2Text Acc: 87.50 | Text2Image Acc: 87.50 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:47 | INFO | Rank 0 | Global Steps: 5170/5215 | Train Epoch: 1 [248160/250320 (99%)] | Loss: 0.731255 | Image2Text Acc: 83.33 | Text2Image Acc: 77.08 | Data Time: 0.020s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:50 | INFO | Rank 0 | Global Steps: 5180/5215 | Train Epoch: 1 [248640/250320 (99%)] | Loss: 0.852196 | Image2Text Acc: 70.83 | Text2Image Acc: 72.92 | Data Time: 0.019s | Batch Time: 0.363s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:54 | INFO | Rank 0 | Global Steps: 5190/5215 | Train Epoch: 1 [249120/250320 (100%)] | Loss: 0.407889 | Image2Text Acc: 87.50 | Text2Image Acc: 91.67 | Data Time: 0.019s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:15:58 | INFO | Rank 0 | Global Steps: 5200/5215 | Train Epoch: 1 [249600/250320 (100%)] | Loss: 0.440937 | Image2Text Acc: 89.58 | Text2Image Acc: 87.50 | Data Time: 0.020s | Batch Time: 0.362s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:16:01 | INFO | Rank 0 | Global Steps: 5210/5215 | Train Epoch: 1 [250080/250320 (100%)] | Loss: 0.286732 | Image2Text Acc: 93.75 | Text2Image Acc: 89.58 | Data Time: 0.021s | Batch Time: 0.367s | LR: 0.000000 | logit_scale: 4.605 | Global Batch Size: 48\n",
      "2023-01-14,21:16:03 | INFO | Rank 0 | Begin to eval on validation set (epoch 1 @ 5215 steps)...\n",
      "2023-01-14,21:16:27 | INFO | Rank 0 | Evaluated 100/638 batches...\n",
      "2023-01-14,21:16:49 | INFO | Rank 0 | Evaluated 200/638 batches...\n",
      "2023-01-14,21:17:11 | INFO | Rank 0 | Evaluated 300/638 batches...\n",
      "2023-01-14,21:17:34 | INFO | Rank 0 | Evaluated 400/638 batches...\n",
      "2023-01-14,21:17:56 | INFO | Rank 0 | Evaluated 500/638 batches...\n",
      "2023-01-14,21:18:19 | INFO | Rank 0 | Evaluated 600/638 batches...\n",
      "2023-01-14,21:18:27 | INFO | Rank 0 | Validation Result (epoch 1 @ 5215 steps) | Valid Loss: 0.238827 | Image2Text Acc: 92.72 | Text2Image Acc: 92.00 | logit_scale: 4.605 | Valid Batch Size: 48\n",
      "2023-01-14,21:18:33 | INFO | Rank 0 | Saved checkpoint ../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch1.pt (epoch 1 @ 5215 steps) (writing took 5.584172010421753 seconds)\n",
      "2023-01-14,21:18:39 | INFO | Rank 0 | Saved checkpoint ../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt (epoch 1 @ 5215 steps) (writing took 5.848175048828125 seconds)\n",
      "Exception in thread Thread-1:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/pai/lib/python3.6/threading.py\", line 916, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/home/pai/lib/python3.6/threading.py\", line 864, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/pai/lib/python3.6/logging/handlers.py\", line 1476, in _monitor\n",
      "    record = self.dequeue(True)\n",
      "  File \"/home/pai/lib/python3.6/logging/handlers.py\", line 1425, in dequeue\n",
      "    return self.queue.get(block)\n",
      "  File \"/home/pai/lib/python3.6/multiprocessing/queues.py\", line 94, in get\n",
      "    res = self._recv_bytes()\n",
      "  File \"/home/pai/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/home/pai/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/home/pai/lib/python3.6/multiprocessing/connection.py\", line 383, in _recv\n",
      "    raise EOFError\n",
      "EOFError\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 执行finetune流程\n",
    "!{run_command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 验证集效果验证\n",
    "\n",
    "为了验证模型的效果，我们提供特征提取、以及图文检索任务评估的流程。更加详尽的描述，也可参考Github readme的相关部分说明：https://github.com/OFA-Sys/Chinese-CLIP/blob/master/README.md#预测及评估。\n",
    "\n",
    "### 1. 计算图文特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2023-01-14T13:18:50.103797Z",
     "iopub.status.busy": "2023-01-14T13:18:50.103514Z",
     "iopub.status.idle": "2023-01-14T13:22:17.287273Z",
     "shell.execute_reply": "2023-01-14T13:22:17.286358Z",
     "shell.execute_reply.started": "2023-01-14T13:18:50.103769Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\n",
      "python -u cn_clip/eval/extract_features.py     --extract-image-feats     --extract-text-feats     --image-data=\"../datapath//datasets/MUGE/lmdb/valid/imgs\"     --text-data=\"../datapath//datasets/MUGE/valid_texts.jsonl\"     --img-batch-size=32     --text-batch-size=32     --context-length=52     --resume=../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt     --vision-model=ViT-B-16     --text-model=RoBERTa-wwm-ext-base-chinese\n",
      "\n",
      "/home/pai/lib/python3.6/site-packages/OpenSSL/crypto.py:12: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography and will be removed in a future release.\n",
      "  from cryptography import x509\n",
      "Params:\n",
      "  context_length: 52\n",
      "  debug: False\n",
      "  extract_image_feats: True\n",
      "  extract_text_feats: True\n",
      "  image_data: ../datapath//datasets/MUGE/lmdb/valid/imgs\n",
      "  image_feat_output_path: None\n",
      "  img_batch_size: 32\n",
      "  precision: amp\n",
      "  resume: ../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt\n",
      "  text_batch_size: 32\n",
      "  text_data: ../datapath//datasets/MUGE/valid_texts.jsonl\n",
      "  text_feat_output_path: None\n",
      "  text_model: RoBERTa-wwm-ext-base-chinese\n",
      "  vision_model: ViT-B-16\n",
      "Loading vision model config from cn_clip/clip/model_configs/ViT-B-16.json\n",
      "Loading text model config from cn_clip/clip/model_configs/RoBERTa-wwm-ext-base-chinese.json\n",
      "Preparing image inference dataset.\n",
      "Preparing text inference dataset.\n",
      "Begin to load model checkpoint from ../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt.\n",
      "=> loaded checkpoint '../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt' (epoch 1 @ 5215 steps)\n",
      "Make inference for texts...\n",
      "100%|█████████████████████████████████████████| 157/157 [00:07<00:00, 21.66it/s]\n",
      "5008 text features are stored in ../datapath//datasets/MUGE/valid_texts.txt_feat.jsonl\n",
      "Make inference for images...\n",
      "100%|█████████████████████████████████████████| 932/932 [02:56<00:00,  5.28it/s]\n",
      "29806 image features are stored in ../datapath//datasets/MUGE/valid_imgs.img_feat.jsonl\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# 为验证集图片池和query文本计算特征\n",
    "dataset_name=\"MUGE\"\n",
    "split=\"valid\" # 指定计算valid或test集特征\n",
    "# 指定我们刚刚finetune的ckpt路径，也可以指定预训练ckpt路径测试zero-shot效果\n",
    "resume=f\"{DATAPATH}/experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt\"\n",
    "\n",
    "run_command = \"export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\" + \\\n",
    "f\"\"\"\n",
    "python -u cn_clip/eval/extract_features.py \\\n",
    "    --extract-image-feats \\\n",
    "    --extract-text-feats \\\n",
    "    --image-data=\"{DATAPATH}/datasets/{dataset_name}/lmdb/{split}/imgs\" \\\n",
    "    --text-data=\"{DATAPATH}/datasets/{dataset_name}/{split}_texts.jsonl\" \\\n",
    "    --img-batch-size=32 \\\n",
    "    --text-batch-size=32 \\\n",
    "    --context-length=52 \\\n",
    "    --resume={resume} \\\n",
    "    --vision-model=ViT-B-16 \\\n",
    "    --text-model=RoBERTa-wwm-ext-base-chinese\n",
    "\"\"\"\n",
    "print(run_command.lstrip())\n",
    "!{run_command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "产出图文特征将保存于`../datapath/datasets/MUGE`目录下，图片特征保存于`valid_imgs.img_feat.jsonl`文件，每行以json存储一张图片的特征，格式如下："
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2023-01-14T13:23:11.828610Z",
     "iopub.status.busy": "2023-01-14T13:23:11.828327Z",
     "iopub.status.idle": "2023-01-14T13:23:12.128343Z",
     "shell.execute_reply": "2023-01-14T13:23:12.127477Z",
     "shell.execute_reply.started": "2023-01-14T13:23:11.828584Z"
    }
   },
   "source": [
    "{\"image_id\": 1000002, \"feature\": [0.0198, ..., -0.017, 0.0248]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "文本特征则保存于`valid_texts.txt_feat.jsonl`，格式如下："
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "{\"text_id\": 248816, \"feature\": [0.1314, ..., 0.0018, -0.0002]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. KNN检索\n",
    "\n",
    "对于小规模的学术检索数据集，我们提供一个简单的KNN检索实现，便于计算文到图检索，在验证集3w图片池的top-k召回结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2023-01-14T13:24:10.023574Z",
     "iopub.status.busy": "2023-01-14T13:24:10.023294Z",
     "iopub.status.idle": "2023-01-14T13:27:02.536015Z",
     "shell.execute_reply": "2023-01-14T13:27:02.535061Z",
     "shell.execute_reply.started": "2023-01-14T13:24:10.023548Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\n",
      "python -u cn_clip/eval/make_topk_predictions.py     --image-feats=\"../datapath//datasets/MUGE/valid_imgs.img_feat.jsonl\"     --text-feats=\"../datapath//datasets/MUGE/valid_texts.txt_feat.jsonl\"     --top-k=10     --eval-batch-size=32768     --output=\"../datapath//datasets/MUGE/valid_predictions.jsonl\"\n",
      "\n",
      "Params:\n",
      "  eval_batch_size: 32768\n",
      "  image_feats: ../datapath//datasets/MUGE/valid_imgs.img_feat.jsonl\n",
      "  output: ../datapath//datasets/MUGE/valid_predictions.jsonl\n",
      "  text_feats: ../datapath//datasets/MUGE/valid_texts.txt_feat.jsonl\n",
      "  top_k: 10\n",
      "Begin to load image features...\n",
      "29806it [00:07, 3768.32it/s]\n",
      "Finished loading image features.\n",
      "Begin to compute top-10 predictions for texts...\n",
      "5008it [02:40, 31.17it/s]\n",
      "Top-10 predictions are saved in ../datapath//datasets/MUGE/valid_predictions.jsonl\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# 进行KNN检索，为验证集每个query，匹配特征余弦相似度最高的top-10商品图片\n",
    "\n",
    "split=\"valid\" # 指定计算valid或test集特征\n",
    "\n",
    "run_command = \"export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\" + \\\n",
    "f\"\"\"\n",
    "python -u cn_clip/eval/make_topk_predictions.py \\\n",
    "    --image-feats=\"{DATAPATH}/datasets/{dataset_name}/{split}_imgs.img_feat.jsonl\" \\\n",
    "    --text-feats=\"{DATAPATH}/datasets/{dataset_name}/{split}_texts.txt_feat.jsonl\" \\\n",
    "    --top-k=10 \\\n",
    "    --eval-batch-size=32768 \\\n",
    "    --output=\"{DATAPATH}/datasets/{dataset_name}/{split}_predictions.jsonl\"\n",
    "\"\"\"\n",
    "print(run_command.lstrip())\n",
    "!{run_command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "产出的结果保存在指定的jsonl文件中，每行表示一个文本召回的top-k图片id（按模型相关性预测分数由大到小排好了序），格式如下，这个格式和我们比赛要求的提交格式是一样的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-14T13:27:27.226086Z",
     "iopub.status.busy": "2023-01-14T13:27:27.225694Z",
     "iopub.status.idle": "2023-01-14T13:27:27.528758Z",
     "shell.execute_reply": "2023-01-14T13:27:27.527955Z",
     "shell.execute_reply.started": "2023-01-14T13:27:27.226043Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"text_id\": 248816, \"image_ids\": [286314, 183846, 141999, 936929, 1006938, 162268, 412925, 384823, 877108, 103269]}\n",
      "{\"text_id\": 248859, \"image_ids\": [574241, 175548, 678269, 1059854, 708768, 1003303, 854191, 913653, 495295, 153682]}\n",
      "{\"text_id\": 248871, \"image_ids\": [60015, 807177, 160459, 706996, 161417, 666622, 637011, 77996, 690344, 783255]}\n",
      "{\"text_id\": 248898, \"image_ids\": [946372, 397154, 642386, 777624, 27450, 829271, 222468, 420283, 323919, 937205]}\n",
      "{\"text_id\": 248931, \"image_ids\": [225106, 139500, 349934, 941660, 515959, 818516, 646440, 979324, 94740, 1024375]}\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 ../datapath/datasets/MUGE/valid_predictions.jsonl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们具体观察其中一组case的预测结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2023-01-14T13:30:55.918737Z",
     "iopub.status.busy": "2023-01-14T13:30:55.918472Z",
     "iopub.status.idle": "2023-01-14T13:30:56.283398Z",
     "shell.execute_reply": "2023-01-14T13:30:56.282738Z",
     "shell.execute_reply.started": "2023-01-14T13:30:55.918712Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"text_id\": 248931, \"text\": \"32不粘锅\", \"image_ids\": [227953, 349934, 646440, 204288, 941660, 425873]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7FD2B298B240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7FD29B3C36D8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7FD29B3C3E80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CXL58+dO/9MsvvvjimVOnfN9XQgKAzgshRF1r4rTm0qembmGyHpsfeh84cRK0KWw4Y8ZX8ObNm2macpFskiTT6XQymSRJkk+LTBdlfFgqlMI5q532Qw89Mq7wA1xaWjp19nS7HQyHQwDn0IEQUqJQaMlM4/F4fxwEwf2NB91Wpx31tne3Tq+duXTp0uuvvxnHMQC0Wi0pZZ7nk8lkf3+f70HTdBMVwjDsdDrz8/Pz8/Nzc3P8otPpnDp16gO5PtbaMqBYXaXbd25fv379+vXrAOB5HvdJcsnFysrKzs6O53mtVisMQ2YkX+S0yhjVBC2KQmt9+cmnr169+vnPf/706ikAMLrgYFNRFL7neywmiMA5EEIoj2N6dbBCVvhAviz8NAQtw2xEtcI9VlRYB6L8DlA/ZkXhlBK8OyIQlV8ZAIajvclksru7u729vbe3NxqN4jguimI8Hnuel6Ypu6LOuU6nY60VczKCsAsdAZKstdoJIXxfjacjEYhxmonAf/L5ZwqPHOSJb0IpbGEhdZRgsQeDu+nOvTSZGmtt6M2hU9ZBpx/kOAXMHntu5Y7evD9563RrObdxGIXolKBoYWGp2+5xKhURDTljisJoY0w2nmZZdvfu3evXr7MjxcU+c3NznU5neXl5bW1tdXV1aWlpbm4uDENLpu4rN9ZwkEiWMbJDyo0fhpYX8GZrRKHNjTff+KP//X/XWnf6Pekp6SnHxg9QTpQb48mOAKFzp7UlkL7XtkU8nU6tdork9vpGu91utcJJHC/0ev/gH/yDr375v2p+qPRKxXVIgyHC4VhS08j5YPGeCZokCT+miNg86Voj12FeLEdD8faSf/wt6iqFLDODwWBjY2N9fX1zc3M8Hm9tr9fyqayXkRIR2+02m01suWdZRkRFUeze3yMiSVIJj6zVOb8RjNNWWA3F8596ViklFJJw/X4/ifeUUohFmqa7u7s7OzuTycRqx48ZWxcksCrMUYi4t7e3ublZZIUnPLDSabu3N5iOY6V8z/ejKApaURB4yveEEMvLy0opfoqMMVmWsWuysbGxtbV1584ddmtarVav12u1WhcvX1haWjp37tza2loURiABuMIDBZeaCyGCIEBgLwiAYH9/vLDQ2x8O/uRP/uTWndthFK2uro7jqSWH1gghQAiUpURHCWwXQiXn2DKaTCbbG5uXLl1aX1/f3h4//9xzX//61z/+8Y//1Iz6gPGeCcr2TU3EWsf5vl9HVWo1TURpmvu+LyWy3ZKm+e7u7mAw+OEPfzidTvf39/f391klMRfn5ntQhbi58oAV0HA49DyPHw8i4lLZoij8XkBEvvBloDzPk6istQBucW5hkk0M6ueee25+fj4p4sF0P+pE5FAKQdZNR9OtzQc7Ozt5qn3flwpFWXgLApVQUvqeF4Rnz56/ffu253mdVidQkUIPHTgHly8+Zi0VWhdFkelsPE61Nc65tyZv8HPF/ODW7yAILl26xPZ0URRpmnKDzt7e3o03rnNhUavVWllZuXz58pUrV9bW1hYWFjzlsZVpnc2yjC9s4LUWFnsvv/y9P/lP/ykIgl6vUxRFkk6D0CMAECiEACkQ0TpnrQ1kWCo3dESk8/LTuV54Z2dncXFR58Xv/d7vPfH4Ex8crz4wvE8Vz9f0yMY6pVEnMxAxigJj3O3bd2/fvn3v3r3NzU0WWq1Wi1nOkTNuCUDEW7duNSOgdbaj2+36vm+t5VKXKIq63a4xZqKndWzc8zyQLC1cHMfaFH7HW1tbY/YDgFIKAZyFIs329/cHg0GR5VLKMpkpCKUggSCQBCrlK99rtxUfP4oiTwSBCgPlm8KORiMpPW4Ta3VbUkrpKSEgGU6DIODHNUmSyWQynU4Hg8Ht27ehykJxpND3fd/3ueKKd37zzTdv3LjBu83Pz1+4cOHjH//4Y4891u/3uRrDWjvUkx/84Aff/va3o1YLAPb29paWlpxzgogABKEQQqCwQMTetOcREAoSQpB1WZZOJpNpPBYE3Ow7GY1///d//9y5cwCws7eztrj6/ijxIeE9E5Qb+GuDgxlGROPx2Pd9LiMAAGvtzs7OcDj887/4y8lkMhwOp9Op1pp3llLu7e3V5GPzvGS2Qi64arVarVarNnCLomClybZvHcPzAg8dsdK3WjtDZB2hs2SkL9rtdqfTieM47ARREEpACeiMmYzHg739ZBrXOSFqJNAdAksjlILICJAAaIwrdJxBHqiQLIRhyMYlIVhrC5253BHAYn++KIrJZMLJ7na7ffr06Xa7vbGxkec5pwc50JMkSVEU0hPNgD/TV0q5vr6+vr7+8ssvt9vtfr/fbre5JuP82UvXrl27cePG5cceO336dK/X63Q6uS60swBguMhSCs7A8hUrbS0ArYs4jqfTaRYnANDrdXZ3d7/ypS9/7JmPxekEAJYXlz8oYn1QeM8ErcMHdYs0K3Qu3I/jeHNz8969e7du3bp169b29rYfRMw8toQ4CMxMZRvLWlurQs/zpMI68GsbWFxc5DIFDpSwz0hEZVoRUAkhAKE0IyFsdcGD1dXV+V5/c7jFCZIsy5CEKfR0Oh0PR1mWAQgiyrIkCAJEBUIIVXZ+EQCQABC9Xq/X6SJKIBJOCiGU8niWhlIq8HwI0KFjnT7eGymlOp1Ov9+31qZpurOzc//+fTagPc9j157N06IoHNjxeLy9vT0YDIwxURQxHc+cOQMAWuvpdLq+vu553sLCwvz8/Kuvvrq6ugoAf/RHf/TCCy88/uQTxpiwFc3NzVkg55w2xlrrkKSUgVRFpvn6aK2TJBlPhkWW1Doq9IN/+k//6Xgy7nV7eZZG4QcTG/oA8X5UPFOk2SJNRG+88cb9+/evX79+584dng7F6i8r8uFwOBgM2FfgB5rnTIRh2G63o6jHZqXWOssTZYUQwhhpjGLZyXQ0puAACgsSrXVRCK211Q4APOkrVAQEDlAIKUWSJJNs0l/uaa0VCrIuCAOdFMY4Y5zOS+vWE0Ih5taAACFQeaX5SIgAwqFI48T3AyGEyXXotT2hyILv+xIFa2oiMrogIhIkpQxDX2s9nY65VrzTafX7XURksTocDjld7vt+FAXdbntrZ3thYeHUqVNCiDRNB4PBzs7OxsbGdDpdWVk5derUqVOnkiQZDofj8XgwGKytnHrllR8sLM0/o5763vdf/s73vnPlypULFy6snlprtVpRp+15HklwzpEjx/WERMa5LE/Gk2E8nnBSXimxu73zzW9+MwqjdhgVuojCaDQezfX6HyC9fnq8H4LaqmPVOffgwYNr16699dZb165dY2Oc7X0WpcPhME6TNE05LNf0lBcWFthp4H4A9pqVUsYUxwatWGKxecAfzWaGUkoJ6XlBoAJnLMsPRJqbm5tujD0hgyDoiE5cxABgK5ndzEx6ynMIUMVKSv+mKruRUnU6Hd8PTZ74vu8JlSU5OOLGMSWkA4sEgCSV8jyv1Y6yLOOvTEQcCecmSe7EZRXEGZ0sy+bm5popx3a73W63iYhjbQ8ePFheXr58+fLTTz+dJMlgMBgOx/OLC2wG/Pqv//qDzc1vfetb169fXz21durUqcuXL6+cWuMsF39Bz/MAyDmT52mexFmWIKLnSVPopaWlX/mVX0GgQmvf8wGgf8LYCe9MUA5zNmNJfKGFEFprtpC+853v7O3tcQyIM7ybm5sPHjzY29tjSs0vLnBnaqk3idg3CoKAp5khIl/Q/f39F198cXl5cTQatdttrfUrr7zCwU6mIw87ZYYBAGeBpZRZmpncaFGQdb4fRn6Qm3wwGLTb7el0msYJ+BAEwd5gO+pERZxmaU5VJQcJzG0ehDwMQzntGU8Ir+SqIMyybK43z7nB8XjcidrtdlvnhuP21tqwFQShZ6y1zoArS33rOEbtwHETD/+JHXki6vV6nAiAqvylHsDZ7/cXFxedc+Px+LXXXvM8b3V1dWVl5eLFy9vb21tbW+z2ra2sfPnLX55Op9evX7/z1u0/+7M/O3Xq1FNPPXX58uWVlZVOp9Pt9XZ2diI/yJN0fX399OnTDx48EIDb29v//J//8+XFRU5RAoCxBxHZk4NHnlAdPAIAzjrMzc0FQcDO5l/8xV+88sor1trV1dXz589Pp9O9vb2tra27d+8Oh0MpZa/XW1pa8n0/zTN4qIyXBSTni9nc5LvY7XZ3d7e3trYWFxdXVlbm5npFUaRp0mq1nDOcvJBSBoHHQtQ5B4440eyrgKzzpcch46jdTnXCJUij6dDv+N1ulwRJTwnlgZAgEAQKAdY6a22/3xcKEQkcSUAB0hnSeZHG2VwPFCoJyg/8wAs9pUI/ImutEKhQSSmlQGQPGlCQkCAkVwUQEvGWIPQIbJYZbXLrjOdL9odyrcovUtG6mZ1iOR0EAWukzc3NXm/uySefPH/+/Pr6+u3bt4ui6Ha7c3Nzc3NzvM/9+/e/9Zd/9b2Xv9vt91qt1tmzp9fW1oqi2N7eXlxcXF+/N9fvG1tcvXr17OnTSqqqNgrAEX4o9R4/FR5JULYRAYBrB+fm5tI0/au/+qtr167dv39/b2/P9/1WqzUcDu/evbu1tbWxscGXcmFhoS640loDuFpqCiEQWcVjUWTj8VAIUErkecbpnzSNT58+zfYAO+yiGoDGv/IWUc0gMMa0uh0AkEJiNfkcAPivVpvRKJtOp8YZYYQXeMPJUBNYBEIkBCJy5bNjnTPOICJ6QvrKk4A6L4qMPM/rdvq+7ztNgR9Ya5Mk6Xf6wvM4aColKqWIhASLiAIkF7fbaogmVzfz+TvnOCvLtc/cI1Y7kfzV2Clkd5tjZ+whcQJd6+Jb3/rLVqv19NNPf/azn97b23vrrbd2d3eiTrvba3v+mW6vvbW1NRgMnNXWFD9+9ZVXf4Lnz5+Pomh/sOus7ff79+/f/yff/BI3/kqsKvdOWFUG45EEDcMwTVMpZbvdNsb8+Mc//t73vvfaa6/t7++zAgrD8P79+z/+8Y93dna4D5VjQwct94hSSuN0zc6mEDXGsKzlECAARFF0//59RGKbbGdn58GDBwsLC1rrvb29euwER5SgCiOEYUjGOkdFUZhCgwcCkW04oSDNkjt37jz17JOJSchSnqQGQPpe2Ipk4DuBBCA95Xt+lmW+76so8j0PidLJNM1za+1K+1yn1SKHxmgZtqxzeVpQm8rYAbMKACRKUIQgQDbVBT9RUkruU+MABdOuKIokSaJWp7aJayuIoxYciuLABROaL92pU6fSNP3hD3+4urr69NNPf+ITn3jw4MHu7u7+/j46Onf6zNryyoMHDwaDASIKBcPhcGFhfq43d+fOnUuXLu3v7165/NjVq1fZ4qSDEtuTJz/fWcVzSHxnZ+fP/uzPvv3tb49GI8/zFhcXpZS7u7vf+ta3OPjMxRBs8E2n0yzLWq0WV3bFceyHZcVNHWmqUqAijuO6aI1JPBgMNjbWubiBy8O63S7fPw6214ITAHgHNj+QhAAJVWBVG+2MCVutSeZ+9Morn/jU81mSEVnf93PrwnanMzcfRG0QBOikVEHgEakgCFphIIAmo+lkMrHWSqWitQgA8jSz1inlBwolqjAMOTomhJCAiCikILDWWiF9jjNUUTCsE7a1Yco05W9U78lXBqtJBdxvxHk7/pW7cucWF7QxrVar3W7v7+//6Z/+6aVLl1544YW5uTm2TdM0dc51u10iiuO43+pfvXo19IOdnZ3V1RXnnNPmH/7Df7i4sAjgnD2QmlLKE9hm/UiC8pW6du3af/yP//FHP/pRt9tdXl4eDAZpmr799ts3b94cj8fMS47SM43qgbnsumqtlS+xUQPvqtk9rLUZ9f02xrTaUZ7neZG1Oy0UbUCSSqysrNy7dw8FCERAInCI6PkqjILR/sAYEwWt0PdQKSmEtbbIcumVFbXXrl17+/bt1kIHpQvDcBDHwlNe4EtPgRRl4FPJ0A98pSRikWXxcJKkSafTWV5anu/NI0gA7QkJjoSSoe8DQBl5VVJKFEpKhZb4IQQhUAiUUlgLPJxLCPR9r+4CshasNUQuCHzrDijCF4dpvbu7W0/p4KsXhmEURQ8ePFhbWxOeiseTKIpWV1fjOP53/+7fffKTn1xeXm6F0Z07d5IkWVtZXZxf2N7e3p/utqJAaz0ej3u93u7uzt/77f/2U5+8CgDWGOeqUn8CgSdxDsAjCaq1/sM//MO//Mu/TNN0cXGRBR4R3bp16/r16zs7O8vLy6x/2RLgGlUhBOdL2ONZXl4ejHbrvCirPqaocwbAIVKe561WqLVxTgoB3W6Xo6Tcqc21jCxoXTVFrVaFdZqkLPeyFqlUuRzi8TxPa/3yyy9//oufJ+UIHSqJ1oIUXuD3+/25bi9UXuD5AhD5I7QpigKsi/xgrtvjh00I6UultQYiX/paa195SikvUEKiUEoqNA6PNCRhY1wRny2bpFzfXps9WA0zqhvxpJRzc3P8Fi59BwDf9z3Pu3DhXJIkcZxHncgTMk1jAHfhwrm337597uzZbre7dmqFO5DyIiWwvV7v1q1b/X5feUJr/dlPf/p3fud3Dm6/UkIIZ0jIsqbnhPHz0QT9t//237766qt7e3vdbpdH5wwGg/39/e9///v9fv/y5ctcwMH6i1nCpiFTkxnJjdLYGLDT1H2cjOYSdDYolVKbm5sLCwvtdpsDMa1WK03T/f39ZiMlH4onsSzML8Zx7AxxqjP0I05CErqiKDrznbDn37p164t/+4sGdVbkYdTKADzf73a7K2urp5ZWAqmUwDRJbGF1XlhtkMAUOo2TeJKoZEBE3XY3aHlaayUlG9wSBUs4IVF4QkoJlkoteVyXXx0V8X2fqwf56SKCmtN1IZgQgudocoCiboQCgOFwyKGPLMvSPOY/cRPwxsaGc25paSkMw3v37gHA2tpaXMSbm5tcBnr27Nl/9s/+Gc9QgLI7pXyYedEYomPWjvn5Am2ai8AHniIpRZwmraj1//pf/98/+clPFubmjDHJNA79wBTFtWvXXr9+I+q369xxfZQ6XFpr80OfUd2Ams2cWfF9X1aLlfCjzBKIrbF6Zlrz45oBdpYoW3v7jz/++N17d1qt0FrT7rVQ0d5gJ2ipK08/+fgzj8lQjJKJ9MTp8+fOnz9rkoL9s+3t7SzLlPIFQZ7n8TRJ4ng6nU7G8c7OzmgwEEL0ej3AvB11WmFnsb96fu1iv72g0I9kJEBGQcsPFCJxeSR3pLXby1zSxoV2bCKzIOS4KT9pvJGvQx3ohUafHYcn2eypVT8RCSVtY5GNejsrmel0urCw8Nxzz12/fp013nQcj8djrXWn0/nX//pfP/bYYwCQ5/kHVfH+YUOJwCdjUCkUQmvdilr/2//nf7t371673Z5MJgDQ6/WKLL99+/bW1la33zNg60tTkwYq+wka7GyStf4rVK4oK8H6Qtf5d+7qakam6h2a763djl6vd+/evbm5OSJL5Iwxk/Ho489+/ImnH1OhDz4lWWqtlZ5AR8aYXBcSRRRFS0tL1lrPC9BRmqbjcLKPGMdxlidpmuZad1ot3/fbnc7C3KLTMBwObXr71LJeWz4dtkOrnazK86r6XSEEsEbmpG7twteBJ74CTXMFGrFhqtqYGMzg+iLzQ2sbw8Cab6xVUJqmd+/eXVlZuXv3Llf28MPPUcJLly5xePXDItQHDQUAWZZFnQ4AWGvfvnf3T//0T1udThAEWmv+bsPh8M7du8PhcHFxUedJk5f1i/pyw2Fq1lV5df1o7cnyW2qpCVWWhffnv9Y+fj3Fqg7R22qi2v7+frsTGVNwCn5xbv6ZZ56ZW+jHRZyaRCK2g5bfCn3fl9JbXGx7nuecyxJuxyEuXlk7tcocstYCiV6v12635+fnA0WeF+SkJeo4jh/oBz4Gy/3l+iHh+mAhAcAJAcYgNdrlOMwJ1WjCmny1xVLLTv7oujqMrWeo2kTZqVdKkTswVZvJOYbneXme37179zOf+cxwONzd3WU9w17XH//xH//qr/7q6dOnP8CWjA8byuQ5sxMAdnd3/+AP/iCIoiiK4jj2wzD0/dFotL6+nuc52z0EJclqSfmwBG3anc1iNqjoCI1iKN65nKMiZb2xKXTrsRlQEZ3VqJQSlLe0tDQej4WAXq+bx/mnPvWrly8+dn/zriHteX4UtArKc13sbu/leb4wP8/1mmwEO1eeyfbGZhzHgNjpdBCxyA0PzWoFarg/dAUsLi4XU7N+b0M6dfn8Y0rUgUnmCl8NxdmN+umqH0iqZ8s4bkrxRaOMsH5c6324opQ3cu02E7ROOzUfV750tWRN03QymaysrIzHY2eIHwxEfPPNN7/73e9+7Wtf+1Co9OFAKR4zRAQCv/+Dv7527dry6ipP32PxOYmne4N9tnKKogB1yPSsXx+xO4/cmCM7Q8Wzup+6aYfxFlfNr2I6chyqqdH4pxSScwScrQFH8/OLV65cmSaTSTbWVgOREp5zLs8Lsze8f+8eW7fl0SzwGlM6y422xhghZSvq+F65arTOLFDpA41Go/2d/X600LQ3EAUiIPAIbVfbHlSFNuuoAjaWaXPVwKk6vlsrFkS01fiu+vvWO/AB64vJJOadOfrB8vLmzZtXr15VSuW6YMnCvt2f//mf/+Zv/iYT+gOk0YcHBUKQc1prlOL73//+ytpa3ffNfT8AwMWLURQppQqnj3VR68t3hJG1W/Ow5wSNcdH1PYPK3Dwiklle1ll7aASbNjY2zp07l6TT8XhC4G5cu/6lL/3a888/v7O3vb69Ps2mIMELfKmLPE97vZ6rZxJpZ7Qu19wlURkhWDMMAIo47bXn8rS4d3d98/42klhaWOi021Y7z5dVfRZJKQAdClETq3kROLQEVaUim6dsqjbJR1UyCRE5JFK7jEwvIcSRTvH68rINkOd5r9eTUnJ9dG3HO+eklAsLC9euXRuPx3WY5eRDQVXJNh6PxuPx0srK7u6uHwb1Nycql02OoogePZH5yFWr6ViHKo/443zVanngqtpnNrzqAzaPA5X84C186Y1xADCZTAis0yZohW+9dee//Jf/8oUv/urqykpvvrc/GU7icWoykcS20GmScoEpACAAIgZ+GKrAGCOlp7V2ho2QckK2FIEp7HBvMNwfhWF0dvXcuXPnwjAEH8MwrDpVSErJxoIuDo2RYdIf8Rdrr7zJZmokk7h1KYoi5hwAcFaP5Qh//fr5hMboZADgzi3f93d2dvgE+KLxUi+j0Whzc3N+fv4D4M7PBAoAwigioP37d1ut1vr6eqfXdc6laeoFfhiERVHEaQICuV74YPjwQ5ISDlOT0bwHTXo1NR3fElkN5IAG3ZtHq5U7NMw7GbTOnz9/8+bNpYW5drvd7rR83/+P/78/Wn9w/8WXrj7/wnOLy0vjeJLrrDA6LdLETPOs4LSh1toW2pgykkDW8WtBQERFYfI8n9rxg3sP1u9uhn70+ONXzpw6y70DPL4h9HwS/L3AAVlrpIC6TYUFZPO04XDQDaqiQahGVPAOdS4XqyYC3kdrzdP26stSP+F1FE8ppbVeWlri+SK1COfpfEEQ7O7uPqwDTywUAKRZGkWRtXZvMDhz5gxbnFEUDQYDZ2x/fj7PcyAyziJiGIXcXcSuRlPQQqWOmx/AyqXWzrXNVDc2sZfAf+J4cp7n9f3jcD0f6kjVDzPeGLOxsdVtt7XW1go3nqoUUeEPf/DKa9ev//EfLz321ONPPfPkuYvnlxdXPM/XkPGg0JKg2tVyLk9ylt9FYfZ2djc2NsbDyfqd9aIwFy5c6ra7vai7sriytLA0Ggy9JV8CakDpe0IIApQSlFI5ld4es6FpSnK4oC6cZd3S7Xa5qZXXueLMHL+FJ5zxBay5zn4ks63VavEsBll1MvHRuLuwDvtPJhMi8jwviqKtra21tbWPUhwUsHS0R6PR6uqqtkZr3Z3rZ1nGlspkNA6CQOdFURTGWW664DZINqcQud7seNVfS9naPYeGaYXVKP9mAB+rIqCmhG5qtKY2dOUaXMT+rrWEQgoiq5zGYmtje2Nr88///M/90OsvzPfm+mcvneY1e1qtju/7nuByDZyMxuPxeDQaJ9OY+zCHw2GWZWu9UwKcABX5UavV4c4NX3mT4Sj3/TAM/YhbqZR0B2sFNX/WXjbbtfxgEBEXf02nU7ageCN7jc0nsP7utaYuM1hVYZ4QIooiHvxZi2GopwZZ4g/lOSJc2fMRi4N6nmfJpWm6srKytbPtEHzfT5LEkUOHYRieO3fu/v37vJrdeDxkw6v2u2vRCMc577V1dUT783uP5KypMV+zuT9VI4Pr9x4cHKQgsMBLlgAQADmUYK32Qi8SUdBueYECCckkGY0mL3/vZe50U0oBIIernLFMPqeNkj6XCfte2J3rtf0eRhh4Xui1AxWMh+N0kizOLXKoKIoiPwp93+eGFZTCamOr4QgsJvlCcdaXk2esuHmJFmxUeLG5GYZhM8QGhwfoGV12g3HsiX0jXvOFCcoSl2+Q1rrIyv5ERByPxy+88AI3MX/ozPqAcND11up0UIpOp5PkGVcrTyYTp02/33/y6aen0+n9u/d4VhZUs+b4UYZKTUNDxdcv6jjRESepvkZ1AJ+FTdOFr3dubnnopwJi15aYyUQWLRini8m42MhRiW6/15/v+VEIiJfOXW4eQbhSTkdBq0z/WJtnRZZleZ5roYN+Z67bA4A4TiQIXRRkHdiydCNNU+WHTE0hBAgka5qC01ZDuUy11lv9QHJVTRRFRMQdrSwOa1NHVLO968fbWktQxpjrECnvVgeYsSrN4dhLMk05hsB68umnn261Ws248gmHcs6hEIh45syZb33n23OLCw5hY2Oj1+vZatjp2traxYsXJ5PJdDxptyOWfPUN4N1YrD5siR550dRc0Li+TYLWDhBUkpjvDRxHUCWcYyqDIxbkRCDAU6rdbpOgNE90rpMki6J2u9MdDccAwMX3/EUUSkR8++bbRCixzExaaxGlr7ztZMsTSgAWaaa7WTtqCYI4jjlz4fu+UGnpWQsEEgLrfmisbW42MZmRrIsBgKVgkiRUjT4kIi6ijeM4iiJ8yAdFRFk98Bx34yvDljo2VnRlO5U7aZ1zPJrP9/3HHnusVncfCZQr6ADAyspKu93udLsAMJqMp0kcBaFSarQ/WFpYfOKJJ6SU137y2sbGOjd7cC6Ol1xmrfSwCw8PZeGbfMWGK4qHE3dHtkMjBXVEEh8kYJD4p0MCB8PpNGqHKlBSea1W0Ol1EXEyGrXDVn1ixjiT6lSn1trAa7F2NpacI7AgFQopAIQzJJTU2k4nSeCFvlTT6RQRK4L6KEU9SExVJcj112QtH4Yhe2a2Wne1DohyVpk5hIjcB1I/z6JRkieEMM7WcqGuJuG7UF/AerAF5425R8/3fR4CBR/+lN0PEAqFACDOG126dGlvOAiC4Pz583fu3GGbHQCcc2EYPvHEE77vi1eAx4Rw3J6JwqENPmKTdnDYXoRGYJk9hjpQ19wfGgKjvj21V4SNWBUi8sR4RJQCEJGQuDdpbW0tSabD/ZEhM7+y1O33kDBL83icQmVXOAfWWrKOiPJcO22IyPN8z/OUkAKEABH6UZrkrRCVKMcvhp6fJIknlfI9z/OE8kFgtWgY+I210bAqA3XOTSYTDhuxomALNU1THtfIflKv12PVz0uu16GM5gGNMeU85cNXg8nKgrOenmyttbo0QKMoeuqppy5cuEBV0OojAV66Bi2R0/rixYvbf72LiGfPnjXGbD7Y4MgFEe3s7y/Ozz/++OPLiwt//dd//frrr3MkWUrJ7uGRzAQ2/JuHf4VGEq+OMJcndHgQTe3O1wQVjd6mplPFUXdAFszu7dt32t1Wv9+Xvuz0ur7yQKCn1Fyvn+c5q9E8zViVS0ApBAgphAj9wPM8AOEc5Lk2erq/vz/X67WjVp6lzlobtbIktb4vjPY8D0RBCEToiAAg8EtbvKYXa3nOrfNYOS6BZU88SZJOp8OsZeuQrVX+WV+f2jrXWvMy13WUg1/U6p55X1a9VHPjfd+fn5+/cuUKJ9I+QhxFssTrATp0e5PdH1/78Z17d6TElZW12zdvbz/YEUZCAfEglU70Or2BTKy19+7d+9GPfrjxYF1KGYa+tdpXQuscEQPfQyQ2faRCAd0DjUzCAXv01cpRVaNW3bElYNwMMNUErdt3oHH7EbGl+s1ol5SSBFoyIAgVabDLpxY7/XZBOmwF2uQqk4hiOkkG+/smc4EXIKItrBDCIUgpPd8HBQ5cWXJaOOcct0YJIXgMLC8XBABs2LH9XbavdMpW916vF0URS0oA4PAkO0Mc92F176sDB5HXgQBCIlLKl1ICCDY3ySGPWIsiynOdZbk1JISSypfSQxRFUeSmyLJE69w4rU2e55k2eZYPTp06NTe3gE7+L//L/6Pfnw+9dpEXYdCCE9ffcQxUOSYVAQDCMFxcXJym48LomzffeOKxJy6fv/yj7/9oa3d7eXE5FNHu7r7se0VRLC0tfOYzn1m/f+/mzZuj0SDwJJtJAM5aB0Dcw+4pH4iHlrMqr2P2ZS0ZELGJilgXRx4y4KCyVh8VecmNxqqt5yDJhLzcNJGw1bPRDNY4QI6eWmsNAHLshueLoC0QJVXyPc8L9nmpClLWqR3B9W+VQ81CK2p1BYExJh5PksnUWssR9W6rTUQ6y5PJtJ49gYgFag7pB0HknMvzPE2yoiiU0p7neV6AiFJ4hKXznqZlk2cYeEIoAmGMM0ZP01hrXRSZc8ah1rpIszhJkouXTmttlVJf+Fu/trq0CqAs2TB41KJN7qStAakAAbFslgq98NSpVUPF7v6ud/HCg60HgtQzz3/s8qXH33rj1vbeTqffSYXpdFoALSnwwvlzTz/99PXrr71x/cbm1oMoirrtVhAE2uTWOiJ0DpN4zNEoDg5rrbM00dbVc0YBAEjUOp4EEp8TQElURCrd7ioXTwCAzgIAFCYTQigUiJzmNiQQBFkyCEiKpAAOkQOVtgQ71qx5yRQAaLVGRAvknCH0kCxKISUCqFq8YVUojVVnqawareqpfQAQxzEbnaxteR6YEGI6nTYPpZRiaRonY62tMSlAyrITEYMgstY6B8YYINF4OEUYyjJoQcI4W+R5lhWZ5rJ8Y0wRp9M0mwK6Viucn+/3ep3pNLl06bGvfvWrfIiiKLzgkSvlnTQoAAACIcACIOB8ez5bzsbTcavbAaDJaJKZOOy1Lj91eW9nfzIajQdJp9Mhosl4TERzc71Pf/rTzz7zsb29vevXr9+4/lqSbM3Pz3e7vaIoRsPJ8so86740KwWP58tQ+tZqAEAAdyQORV4z2AQN76rpM9XCEJxTSpHnAQoy1gKhI0RhrRZCIggppfJEYesIABGRMVrrvChyARJBgiPnjCVHJNAiokSwRJLIYrnIIiIKpTwpFW9xzgkBRFCv2wYAQojxcCSEYPtSAJJ1WZKCI8/zUCDIauJpoaeFRkQ/9Ew1MQoRWZgqpfJcA4DRjohjUj5PFPU8ayw30+VFURS5yXRhjCmKzBiT6VjrQipstaJ2r9tqRdbaxcXFl156aaG/oJ1VQkZBdBwT4KG1ok8EVC3UCXgdFwhbIQmyVp+7fDZL8ru37+6MtxbnFi8unosni+qtDZ671O12pJRxHDvneCrO6dOnP3n16q2bt2/cuLG9ted5Xr+3OB4PRDn2UtVUM0bXl6M2g9iPqiOj0LBBa7ueqiQnHBgAlgAJBAI6dOQcAJIzvIMEJ6VU1YKToixM0UUFAVIppYRyxFWV5JxAR0Kgc8Y5ZauckKjmitUL+tbh8ToWQUS2cm6YoxzsrOcjN12TMpKQcThJCcHXR+S5znONiAJVbRGpanGtJB1y0JRrWfJqrupwPCh0BkD9ufbC8kKrFeVFOpkO1pbXut3+5UuPAQhjcocQeC1jnZInS5U/CgoAwAEgSJSGNCJwQ22Sxcn2tN1uX/nY5dFwsrG+mejp0urKFy/9yoMHD+7du7e3N4gnU61zIZR2NooiIgxWo9WV0y+99EtbW1s3btx46623jLGIzlrH/KvUnAnDkKnJUaHazbeNjFGlkV0dbal9+YNvIMiBtVaQIAIC5ISSAyRHxjkJUFYqISIBOTLa5MYUzlnnDNMYBVprWZY7MugkWTC2MFa4g4UtHYcbi6Lg3EydQqxPFRE96TtrM2N0UXBdiODBqNNpMzYkqznLaaGdc9aWnXFlGwmqTqdd9cWXCzslSWKMKXQCALry1tMsLrS2TguBc/PdqBMFgULhCp34oer0lhf6SwJlGIYOXOS3tTVweNWYCidRfEIZqNd1WzQhgi/8Vjt86+7Ndq/jRSp3qrvYDjoXhsPh3mj7rdfuPP7445//lc/xYNXNje2drd39/f35+UVP+YLAGItSnD5zbmlp5erVT21svjUcDjc3N7e3t5N0ypUNYRhMRmMAQEHVLauLPxAAq3X+AIBXOUcsJ8oCEFVdFrxitj2oCkCBKFlZS49bmZGsYyZJFADEhRks+aSUZJn3xjmwRICOSBAZMsBjswVJzvA75wqtUQhjLRcmm6pJv65GICJQZe0BV3XUf+KUZt3nTlXsPez2rHO27C/wvSDgMlPOITsAYzQnRLi3GMDwwfMiTbPMOg0AUoqFlblutx1FYa6zaTwUDntzy8sri8Ot+OzZs91u3xEIBCWVJevhR8oGNcb45CGCAAQgCbLVanW6bRAUp1ODutPphJ1wtbu0dHo+b+nvfe97/98//IML5y+9+OInz108Px6MBvuju3fvC5BFYeJslGeFUiqMola7Pb/YZR03Gg+2trY21h9sbj4YDsZEgOAElbPEAEU5haMyN2sbtN4CD5VFCyEIwDkLlqRUIEFWglaQAOesBWudM5aMdUggkLMswJkCpSy5KhzrHIBgjwSBAIwhIYQ0aC05Bzz5FkBobYVQbIYSkZScKuaQp02SpO5jMdUkaM5GisbiClDFL6fTJAzDXm+u0+nwnEo+H67NYza7UquA76vheMIWZ1EUgK7Virpz3XYn8n2VFclouu95cnFxwQ8959yDzfW9++mzzz7fjfqlJQ0izzIvehRBT6AXT+C3ytNVQmnQBorVldXXb90wZIJWME0mw/FgZW0liiLlqVPn1v7Omd9YX1//9l99+3/9N/9PgerTn/7sZz/9uY89/7Htrd37b99HCePx2BoqqACLRZ4JIVQQnj578cy5S/rj6Xg8juP4/r174/FwZ2dnsLefxBkAKE8opYRXBurrxB37EFx7Co2i+rKh3iMiQlHSxVVBU3CgtY0CnxMrUnp5Ufbr1PFzYww57i8lay1KibwMDAkhQQgkoiRJ5ufnuXqSK+U4x1uvasIlcM45jokGUYsLTXzf58Hy3Bpfu31M01ar1el0Wq1Wd2GZqtx6nrNVqWuvy1ULx9T5oTSfZlmGguYWF1ZXV9qdgMhqqy1p67RQ0O62pcQ4mXB2oNPpnTlzDkAUuvC8AAFbUYsIPir1TI2cLIFAkIAIQoEKPN8U5UgFS3aaTuNsopRSIKSU5y6eeepj//jr//jr11699p//9L/8T//if3zssSvPffz5T1791HNXnx0Nxnfu3N3a2kqSJGiFWZYlaRonCc9nnF9aXlxZfeKJp7TWaZyMx+O9vb2tzc3t7e3xeJzatL4Zoho+qHw/q4rTJDvSiCgEEGlbAAASISccscy+Aroi11xl1+q0fT8UQhhy1pDRzlriVjNyHMSgKGyRQEQgB8ZqMoAFCSHarZ70PYfA/ww5a8kY47B8QiyU3psFAiviybR0mAAAka1VISXX5vV6vW63y5KSz/Pug3XXWLGOpTlVSWBTLVgYxxNW8YPJQAhx5szp1dWl/lx7Eo+1ztqdyDjrBVIIsC7PtC2z82R8v7O0uAI8fLS2nY4n6Ek0Q5E0AbL4B0JjwBjQBszLr35nb7Tb6kfgYa5Thy7Pcy/w19QqB5al8CQgopDSk8L74z/6k3v37u/s7Cz0Fz7+8ecef/zxMGhlWTbYjXlVnvF4nKVpmqY6y4vchDyxUXmc+HauLHHYGe5wyns0Gg2Hw+FwyCtmYKNUqi6hICIOOSPylDkpUbA7jEhpnjl0cwtzq2eWO712ofU0nnhSZVk2ncbxJNGZJgOIqFB2u10SSEQOyJAlLEtdXYLMJy7f5PIDlqN12UedVVJKRYEXBNy5HfHAbw4bcXU9B1BZWHKpUdSdK8O3Ve9bnudal6toJkkSJxMunmLJOo4na2srTz3z9KXL5xBhONw3lEdRaK2WUoIsFzYmIi/wfd+fE2f+xb/4l6dXTyOIA/VdrfqH9e8HP09WHYkqz7F0QFCglEAI2G1194f7zlDUCpSS2hbOuVYY7exsRFHUoV47aoHyAUBbDRq/+lu/rrXe3x/efevu/fv3d76/tbq6eurUqe5iu7MQLtk5Mi7P82SaTiaTLMknk4ktbJ5mWVKAdVV6E9fW1mpZwrFuvpGbm5tc8pM2oLU2lBIRWeMcoENwRIQA4PvKkiNBcZwkcdHrz0eBZy0VeeockUNy6Cw46zgBtrW1w+tfASIokJ70fV8pQCfqalfulGehyOVwvKVJRF6GoS6FsdZaIHJ2Z+NBMyXreZ4KfBX40+mYFToRWavzPJ9Op2kWj0ajPM/jeMrjFKsPCroL7StXrpw+sxLH01wnfqAUBnE8AQm+UGixMNoScQcIES0sLK6urhrnPFHV/n4UMpw1jnlcBAgCN99feLD1gAyhQ095QgKga7fblOg4me6P9qT0er3e3Nx8J2op5U/zAZDoz7evfvr5T4mrWZaNBsPRaCSkYlVKHgRKhJ3e8to8EhSF0bnO4zxJsjROkiRLkkRnObuo9RyHIAg6nY6UkseVsdvB4Nc7+w+cBaO1LthYs85acjgaD1AIQ8aSCZRvtQOBZMFTAflILSnRN4EGhwhSIraiDgkUAlAKVCg95fvK87yuf1BLwMl0joBy0ws0Mgh89QqTW61tljXPk4i63a7fqIhlScmFS0mSTCbj8Xg8mUwm09F0Os2yNEtyIAAB0oN2u93vd/v9fqvV6i32lpaWCO0kHjrSftAhAZpyDz1rLY+N5gcAURbWnj51VoKX21yiE1h3hD5821l8Ht+383OEagbKEVEAOgAEWOgvtIJWohOdG0UISkhUCtXi6ryUS9bQdDqdjsYPNmO29z3pK6UMqDxhI0x25sOF1V4Wa46tFJnO88IUWaYdOZQgUGLY8aJuqHAREcEhEQ2GUy7sjeOY18EYTyfsd9dUKFvwpJACn3jqGQ7Pk+XF/hBcaQmgFEWRTdNpYXJU0oEtiqLVZlVLzji0iCSEUBLQWks8qR3IoQNRLdzryuwAVNyCumMYgcvb6oIjY4wXeq4aoxCGrbpesa6DNsZoXdQq/vWbbyZJMhjuDYdDm+qDG9OSQRB0e+1+vz831+PSE8/zhI9xOja2UEoGKshNTsYGgaeUMo7DbQqltFwBROLxxx8HACk9Y5zvCeIE8kdHhFYS9OgZi47XjoIw06nJDaJUykOQztrClr0vUqmFlQUOoGzvbftSMVMDLxQSnTPaWZPnQeSFwhciREKtrc6NzrQtqCiMyU2RmSLLtQUkIodE1O30+z1RBxfr9kueV1i3C9eO1NbWFoBAR0iiDFqBQMQkSUAgIliwIAjRCiWDIJhMJmyKIaEkiUTWkgNevYBTAJbn4VhLiBrNwUWhRnOVMaZciUYptjiZgsIrG+obitsCwP7+PvdgDIfDvb09Xsg5yzICW5drBXN+u93u9Trtdnthcc7zvCDwgiDwPE9WiZ9Ux8YWABQEnvBEoTNyTnlKSEG5BgCsrpungna7c+XKFQJUQuW6qL7FR5GgFRAAQCBYBKGUjyjIWiLhCUnggIQfBHV2xFpLSJ1Ou9frcb/sZHOEiK2o0263ebWrJM+FEBIk8AqYUoRtX3V8JIFWgEPnwBlXFKZIi6IosljyjWSpyf6QlHJ1dfVIJol/zUxirXXamUIXhXHaWm2I0DmjrbHWWCBLprCFMYUD6nTbiFgux+mQLBAhWMeBJakEIDrhqEoiCEu1zGa3jI1ILhax1rIghGpeaUHldD5brdkMAIjY7nbm5+dXVlbq4dzj8ThN0+lkxKOTEetlTpGHzXLcKU0Tdpi01o4MSdvr9ZSSRZEZWwSBB6gKraHMu3LtLDgLKvR6vd7a2mljjSe9yrI/gWr8naBI5ABcD6EAhANBAAQeAJw9feXug43eYlDYhIrMj7CgcZgvsrZDlB76viwfRqls0GnbyLIrMBrF1o6JqN/vCyE8D5USHNMmJAu5MYZ9SqzK6T0AD9SymmsGBdmltdaOk906+FK784jYDpYlcVVREFU5yWZgvwb/qvTBIs2u6oWCqnXEHe4VJqIh7jue7Ng4ODmaxBP+4ohl8BQVQgRRy4gKJMrecz5UbwG6y9TvYxhGQnSMWUyLnLKcCKw27AJOp9N4kiRpOpqOsixLpylXHyulgnYYKF9CObgKlUTC3FqeJ4LoaWs6YQc0+CAdQWCDTzzxQkuVHcadMAAAiQDHzLwRJy0+X+MYJ6ly6ykMw263m+exCBEAECSRqdM8eDiSxpWRVV2IV4eauX2nFkJ133dzcEDNUQDgVUT4Brfb7V6vx0Q84hvVKaV492COnHMHUxim0ykcRziXFccStBGGPMRpCB0AIYIUKEhwiAAAVlYvHXkXv8j0kL+Lq2pf+K9MPmvtZDLhleLZfnWFNsYUWX4QoIgzlsVZVnA9fNn8hMI5J0XJL6JDktBay5fUWquUH0WhMeby5cvvjxYnB8cHvQSABer4nZXFpZt3d7tRCGCFUFTkVNV2HGEq0+jhKoS66oe5xQFkrDq8RDWtRVZL0c3NzVNDXFlblloGQVBGa0EIcdCLt7LagYMalAMsLXeZGQelnw1SvgNBoXHviagQ+bGXqO7GrJ8K5xwB+VE5Tp9sqUxYcWdZNhqPuXtueXl5aWmJG7kkgTGmyHMmJTtPHAHNsoIjXL7v+34Ijmpn0R7W0/xtPBU4bcvlzlB0ep2nn3zq/dHi5OAoQUUdbyAnEJaWlt+4c50cogFnyBokdXQSE1U1R7V1KBrTCfv9vqsWNq77GImIhxTUTK3fmytdv7dmPCJm2cE8W0RQqnyX1RmfOU9UAueQSLC1ypVQzoFzUPGSvY2KW3xrD9rM6VDhPSFAHpdrhDbJDdWiLQRknTVkrLP8DBRZ7MpyuBIcYLfWFlpzRxGHgXhtE7TOOWe1Yc2gdVmmlGXl5AEut0PEuiO+eb/qXxERrLPWSqkQcTqdvvTJlxZ6Sx84Y37GUI8yPogIEPrdXjvqkLEWwBbGGXLC1YmTii5H++NcYzJt3dTGs5xqodVut5uStVbcaZHWKr45BEY0OjmbtNY6a55DfRqOV7gDIiZs1UmSY7VdEO/DASNLpQ3qyDk6kLitqqPtwLpwRETj6ZQqT6h5/vF02Agn6Xo7n6GnlBTCaJ3EcVEUzrk8TljFc1giy7Ii4xFRQinlSyWEAu665kYu7luqvoUgsEAAQkqZJzkShlGIJHSuP/vLnyU4GHv9EcWxgXpwAJ6QDmyIrVPLpzb27iE4nYEQsml1NWnRtCOh8WTX9MJqBo47PHWxCSLypNe8wXmRswJtdnseGIgAnn8guWsBQ1XDdNM65BcFHdL1TY3fVNm1Lbu3Ozz2QeJUgm2A9xd0cEAhpSdEULVT15YMAPDiesaYPEnTNE2mcZqWyypw0SCHsHwV1J8iq0Xt4CEDlIgkSWutJwMpJVo8c+bclStXTGHhIzOF6XgcEPQg5QkAAAJEUkxDPzh7+tzGzjqSsoXxQr9pn1HDDG2qe2hUHAdB0KRILWUP+tmlrOcdA0CeZiw6PU9F0cE6dJwKp8NmHxGNp+OHHXY6vBpBk4VGHNLX9Z/qYPuRBwZdWO9ffygRSU8SkVAonXDuYJUjVbXb1d8UEUWlSaDiFj9+WZaBBQ5GISIX4XvSr8IA6uA4IAHQOUAihw4ICKHsBAQUBFprX/meUOAQUXzu05/xhE/eRyfg+QgwQQ8kIdcJCwQHYDKDfrDYWhAk0AlLVkFAZJtEpEqO1l5LLc8YPKaZqlEwNR3t4TUSDm6wEtBgG/MBEYLAg8p2tNY6J5lSqjVf73/grDSoeVRCN5hcb4RqkacjxAUAqw/N2K/BxjQcFtIAILFc84nMoem1XlXF7JxTKBAIrHPa8EBnXiYeEQEEV1hzw7GxlogEKikRyjDwoYpYACEqxkdBJMEj66T0XvzE1TROu+3uB8KSnyOaKr4qVuVgPUCn07FgBYjHLj7x6rVXFpYXiqyIImWqFdPYG5BSsr0PDecGHlL6DKLSFX0YpYARHNJCIEIApFKple8iEgKxcnQAoKCDIbEHi2hUy7o9DKtLR/gIlQMVHbvd4YFT1SRu85jN/cFUlozirlHbfKMQgtf0ZKb6vl8KeltWABIZcAQgLPdmlBXYRETc5Gqr6YqueQoOPellcaaEPb12dmV++crlKxKkNtpTH5ni+WPBk0XgwFWisseCADyhCIQFuzS/eP7Mpc3tjagVsPHE0wfYqHLO8SSt8gCHPaf3h4clVt2WdERieRgdS0SEYwgKnBVtsKrmlpPHb8/xgNB02CSot2PV70FEFstTb55n/XUEARMSCcBxn6Ig54gVNmskqAZN0sHESSJCOuwIHr7Axpher+dhMNgd/Iv/27/UuZaBJ/Fk1c69D6hDS9/RwU8iEEI4gMJmc9Hi5Qt47959T5ILnKzW5WB9DQDcR8bHaFpa7w/Nu1JvPNJwXP+VZ5McR1Got9fsAQD/kP1woOKP2Kz1dlcfBNE5J+qO53oJMkRHB9wp/wjlSvQODolbflUGGB4iMX9MFdZEYDuzzJ0f+H8AQAhUtvmL8hwQQj/a29n/8he+fGblDAIWee77j+ow/sjgHZ8wIkCQIBBEr9VfWVzb39/15wQX7dZGmKyWqDriJL0/jrJih4MbcyCSEfk1NZ+k+h4f0rPloQ79Wm1pvqa/+fVh8h9I3Kolo2ZzueVwLNU1QrDoyFXsJMclWIeOSQ550kp9CvzVyyvwEDurqwEA0A7bw+FwdWnl61//RlHkgd8iQ+B9lOpCjsVxQVACABAIiKgLHchQAAKIp554WsmQCxeokjoc6jvWj35/BHWADtDSMS+MI0vA//i1cQSOwBFZB9Xr+tdyIEljOxJYIEOO/72b10cOW//jI9cfwZ/Ig/KIDsTn34jD7Ky2uPp6HjoEABC6mp3UCBRwiu4b3/jGXLsnpdJFEUStj1ZdyLF4hARl4VVOnBEOHDlc6C6eXjt9fe8+j3NptVpczlMnPOCw4Hx/QrT5DnpIqtU/D26PKHPl5FwlXAmRyyEBiASSc6VcAwBDlpf15VVjqOwcIfuI7ejKRlE4CHZUH4F1/oy/LJsDD0USgGrqMYPrL4Dlb0gOnavqYCotf+QKAAmoIkuHnn8SCCKO469+5e98+lOfTfO8FbRAKCAAc2xpyEcJxxEUqxuNEAQBATgSgRAO4LHHrmy6N6fTKS/FztEiImouovBT26DNYoimrm8YyBVFiIiq8jamUePONW/kgWHgRKmaj2SSeIrvw9vRIPDCNI7QEToi5xDKVA4SgXXoCKwD51j1YsnoxgkwHQ8rmcpbOxjyU+188PXx4EpWDhMefDf+mvzYvPjiizx9SQhhjZMS8jgNol9cG9QWRgYKACbTSavTAcBxPO61O+fOnbt79+7+/j7PBLXWck7y0LP+U3vxNY7wvnl8YqvXHXjrUP8DgOavje2mShI6cvVPFpzHblfuYCG8Zpy1/nlkO2E5beqQkCtF4DHb678e3fmhF/jQW+prgoi//du/feH0hcLkkddGkDrNglb0i6HiGxxtxOtlWG7vdcpg71K7BwDP9J8zPiFK5VQ6TJyg7lw7s7nyJUdSkcBaAkeBCoIgoMwdcAsBwCGiw6qGCMvwvqteCKrSx0c8g4p2TesMACwqwIa3DqUIcsTzFw44x9ulLdMKPKNBVALsiNODlUVoVWnwWWtdtagB1XFWQdZahwfbfc1zGImLVMg5sta50o2zjhxbTUSWwDmSeUFVdUtNU1c9gaUsbyx/aPROGLbiUeqLqN9dHuxPV5dXvvZf/84LVz4nADxVplq8yAcwHC38YJjyc8J7LlMtqHj2yedXV1e3N7eRRDuK4jgGR8k0zrKiqgmXvu87LNNIlpwlHi1jiXjYTOmNPupMHhYhzT8d2XiIwe96+8NHO/awRw51IK0fsf3hfdgNdw3UErecWHv4CLZ+HiqRzE81gwil8DtRBxHjOOl0er/8S5/56q9/FX5B8Z4DuQH6BPjE40+nRf72vdvGhZ359nSaoIRACLLOgJMSPV8553KjUZbDAqAaFsABZwcE1FhfgcAdNl4fJsGRjeUWOLr90F8fsf1Rr4/Qi4iOOCXvtGf5AomQGiUjJc+M5ZH4zjlwZbGftVZUD62rAvfsDHHunhDqQDUf31Ot3a39U2tnjYK9ndHf++3f+uZ//09ObD38T4/3TFABnnZ5S7auPvtimqZb+1tSKRJWhJ4SHgqR57kxTkqphJQSCS2zAKvRS+UApodWQuEkc1OIvQOZDviH78CVv2H7o14fQmNz0zdvorkdG9EicGUtAVg2WInKOCgSoXOVPoHDpdNogdULHlyEg4/QdPbMhfX7WxKDf/QPf++/+c2/JwDTPG89curnRxvv+cmz2gQikqB8CD/30ucunb2wtzUAI7JJUqSFM+XVLIpCWwMCDVqD1grn0Fmw9QwZ/tc4jfrfMUThnY7dfjyr3i+a3/R9H6RU3u7gNRGVTk7JzvKziMiSsWQcWEvGAdnKFmg6hdaWJTLW2sjvjQdZtz3/1f/q7/7dv/NbvohybY+y8yMenG/iPUtQ34scGWONUsoHeO5jnwCBb9+/qyl3Flod60W+QOS6SS4Z40AhgUREBIuAWGWZLYAgcURuwjsq+ubdhSouCO9CXh57hGN3O7TdOoCDUHz5j+jR28uQfi3y0JHlmFMZxgeig2yCrdw4R1xOWl4ZWS4iWuebytjFaJgo5f8Pv/e7//Xf/lqqC2NM4IWFdr53rKz5yFP1vRcTkEgmedQOBaAF28L2i898yloajPeTIpmO0zahCpR1hgCUUqrqYRLCIaJgmmLppB+E96BymvCR1DyysSbosTs/vH9z+6NeP8xmPkMirF9w9POR26uCp5qdjpDIOWNLwepcFcsHa6mqHin3Nzwh4iCeAM45BIlY9hf0F/r/+B/97uc+93kBsu11CSBJ8nYrYHoi/QJw8hDeu3FtqNPpSuHpwnjgE6A17tPPfubs6tmW39ZJkUxTnWmTaWecAFF7rNaWqoq11dFPP+zSHyHWkT89vLG587FvgePI/c6f9WjKwjtuPzgCVgq99oqcY6ZC+bo0xA8VgggAIFFeLkNkARwIEEooT3r/8//4L7/0+S+HIpomGUeU8NhJNg2T6SON956NrH0bTqeAs0AODIG7u3f3zVtvbOxuCIXtXlv50hgjWoSISpSdX9WngueVlfboCKHsjAMAFO5RTKo3Nl/k1Th6aJAGqrpJ/vVQiLGKd9JDTg804qD1n5wpe5UasaHyvc1Oj/pX0ICubgUha60zlgi5A4nZWX60sdZaJ8rCBgRprXXaCSF8PxQg9/f3EcTi/HyWFnmef+ELv/a7v/u7a51LRy5Ik4MPSdATN5D2veKnIyiUHHVAFhyBmejJm7dv3nr7zTiNW52o2+0WMqtnCgNAOfJT+ZXwgHoFmrLSGQ963pty5UgZf/1Cw8FyC8396975JguP/FqzDRqUfYig5V+PENRW8zuPEFQU/NeSo65sYSJjTP0aXdUXYCG3Mc9I86TUhU2SzBkrhAJLve6cs3bjwdbi4vLvffN3f+MrvwEAHnQefS8P35pfCLx3gj68OwKBs2ABwIGJbXx/Y/3B1r3Nne3xeLh2YZXHa4Vh2Om0PM/LijxJkm63y/FngKqKnluW4aAVpPzAd1TZBo9xieAdJWhzY/2rbTTOHyFovbGZ0mz2yjX/inljZ24CZVRxUE61OlvGRMOWiKdplmVKqShoCaE4p5anRZ4WzsLnP/u3vvnNb547dcGBkyAEHA0n4bE38BeFo++zahPgKFMJyJKxYFEggZsU4+tv3Lh169Yw2Zufn5+b60tP5XmubRGGYdgOi6KAcgUFIlE5+rxoxzt85kNCVFci/YgQbRK0iXcgKByv4o8naD0IsslX5xxklSQu/14GRPkFGVuX1fH+nhJVdwqSscZYAdJXATlcW1797a/9zpe/+OsSkAARcDAaLPRX3uUdKfERZ+p7JiiBAwCs1lY6dFEQAEGDQSAHLrVJHMff/+F30yLlmb/Kl37kB0EgPZnrDCSgBCEECaotJfUuChyaEvQdVDwcJ0EfVTn/KIJSccBg15hQUo9jqNtBSxs0p/rXxmrKaIwBprctv4Kz1jnnS1WNuFdIgg1Wheo3/vZv/N2/+9+sLixb65T0gLAoCj8IgBo25bHkO3L9/s9GUAvczCUQRKOKjQ8GXLdGWBZqAEDq4nv37t28dWt/sIsS/chHCdppL/BQAngoBICsFhPDso374Y57eIS7XdABEeFdqPimKn9YoNJDKp7yg41HJOgRw5QJipr3L81WjigdENQCVXV3/N7R/nC+PxdF0WQSj4eTleW1X//SV371V37tiUtPpGkWhpFAVWTaDwIgIOPw+D73Zjzk0P8/KoslPArvmaAGNAAiCAECa4ISgAHAimoCCMCCTdJpGAYSpQVz5/7dG29c39x5oALVm+tO8xglCU8Ihfwu7mSI5EH1zaOGQTTPuZag8Ggvnuos4mFbk97RBuW3uFplVyys9z/ixTNlhQEiPNiZl2ogqsVnTVBnyTl3emX13r310Wh08fylL/zKr33uM5+5fOHxQAT7+4PFhSUAYXMjhQcSXe6EFOAf2xPbiChR/YOv4Xu6vScO75mgBWgBgIACVElQNgItgARAAAeF1ShBeR6AA3C2YnFG2YPN9etvvn7vwZ2wHYKqCOohIjiwAMAEPbZx+WGCEpGTB0HEmp1wWJU3/1S3Ix8hKG+vDcqDPz1CgrpHhJmkPagfdWVMHog9d1u6aE2CjvaGV65c+fzn/tZnPvO5y6cvIyBTTYLKs1yCp4KAH/dSWauDKcx8BwGgYmdld/2fmaDvGbqqkUcABON07nLt9H/+P/5TWmRZkRqhla9830eFROQFYK015ABASJRSlrPJJVhrjbXWaeJVlKQUQphMNkrRDpj3qMEKvj6Y/HHErKzfbhtDHguZ2GoUbTlYgeOXlqjKOJBDa60pCq2177WstexaIaHgjg4L6NBqC1w6qh0AdFu9fr//jf/uH509e3ZtbY17l+vu6g+q4vujjg+foPWjjwAIlox2WoP2pTfKhtdvXr997/ZgtO/A8URtDRmPwwyCQCnhnEuLXGttnfF8PwzDIPQAQBvDo40DdTA8o2k+cuf+w5JPla1KR7U5T0k+spGIyOOhfI4jRM45a5yrZ4Zph0TluBRUAJCnhs+EeDkl7ZwhdCiEKLLCGepEnVOnTn386Wd/6Zd+6ckrTxapiarGDGstt3bVTJ3hwydomcwrfxI4A4aAClcoIQHIAQ3zwZ37d27durWx9aA/32XpBeCUUirwlVJSIgieV58ZYywQj8nl5Yuaw3ZqYcmEg4crQsxBnLXpDJWLcTVQE7eWoMD0tQDOhWFYqmzerShXh7e6XGRDkLDWWs2WC4R+eP7shU88+4mPfexjp1fPBEEgQUghsSqH4GFV9cyzmQRl/EwI2ugkIXCO3RogB86AtmAIyEJpw333lW/HcTwajXgeMa9X5HleUWRCyWpeJrq6zYOK5qfVXEzTFI6rEcmcbu5cE5QDYUfELREFTlX+UXUcIiBRFIWz1uhyRW5eHUkIURS2KIosybXWkR+tra1dunDp1Orpl156qR22O1E3DEIJwpETiAjSGFcvSlZPZ6lF6QwfPkEfyr9xJDXVqVBCoQKAmrKGDM+8nxST3d3dzZ1NXiZ5PB35vo88JVRCPTQUEaWvj9HLRGEYNlV87c3k/tHAPm/nZn9XhUXLU0akUaX3DdWJTUHI87a5AYvHIvPCD4GK1tbWrly58vjlx8+cObO0sNzr9VqyxfOFBPDkKVHLyGppkUOYEbTGh0/Q40BV+seVOrMccitATPVUKeXhwe1JTJoX6Z07dybT6WC4v7+/H8dxwaM0pfTbRVNAQuX1p2lKx3n9sToafqpVPCt0HozIzAaAIPGhjE8Rl8wBgAQ5mUyMMdZS4PkLCwsXLly4dOnSysrKJ557kU1SBBQgEFCBB8ArSQM5KIoCSQRBUPUXQH1utlok/MO78h85/HwICgC8VuzDrgCBASZu2fDgnHOWnK98AFeQSdN0NB7s7e0xU+9v3XTONcccl8SqVoKrwb9OsByz2JSvbLPaxojaWpTm2zk7bZ4XeJ4XeH4QBJ7nX7pwcWVl5eLFy2fPnu2HXQDQpLXWUnpSSq8aXS0ATNmHhEEzD0RAxmVZ5rcPFpPg52TmHjXxoROUwOHDHZsOsd5mKzOgjkYBNBN0dFBA1exjIiiDsLkGPZ1Ox+PxaDTilWe11tvb266xlnW9mEHhH0ylqy1OIuIBPvUqJbx8llLq3OLZKIr63V6v1+v35+fm5uY7fVF5NgjgqqSAlFKVC/qUmgERJaryOWFqEulMA4AX+IcimA3wefr+R3w08geEnwVBy086nOo4/gUczh3jMRsbfAUAsI8ew176YZVQZEmZ6PjYnYMgYNXseZ5CJaqzlUcZJOpzEY3TwkeVXdIxScjDx3vU6c8AcNIJemTLIZqWeDdrnPPO/EUVHv+OQhfl3O1KtlfztY8WX1RfQzROR/zNJS6P2mFG0HfEz4Og8AhqPgrvSNBHfi4BHJfoewQ/j3kwiCeOqYPct6h+YHPDu79+Mwn63vGhT+A9XvfxHabj5OXfpO6PCrRH8YMO7V0/hu+0P1b/qg9EhEbzVPOLPKQNmgd5FGah9/eOn+uI6Hd5w8q5JAdw9Q8AiceLoCOy8+C3R0hQaxr8POSoHT3+o83N4zc/dGZHzmAmQt8JPweCHvFy3OFf5eEVwh/FSzqy+V3DPYIQ8l07zY+Ukvhojj7SsJjhb8DPmqAHIfoGsah5Yx/iJTSoSc0N5f+O9+KNNXW1HjeUlJ8lgmP3f+cl2d5tc+SxzD3MziPP5yPl8QwA8HNW8Yep6Q5RsN548P+ane7YXQ5DyeaNP6j7p79J4tanhA1t/0i8g9Qsd3gnds7wN+LnlkmaYYZ3g5l+meFEY0bQGU40ZgSd4URjRtAZTjRmBJ3hRGNG0BlONGYEneFEY0bQGU40ZgSd4URjRtAZTjRmBJ3hRGNG0BlONGYEneFEY0bQGU40ZgSd4URjRtAZTjRmBJ3hRGNG0BlONGYEneFEY0bQGU40ZgSd4URjRtAZTjRmBJ3hRGNG0BlONGYEneFEY0bQGU40ZgSd4URjRtAZTjRmBJ3hRGNG0BlONGYEneFEY0bQGU40ZgSd4URjRtAZTjRmBJ3hRGNG0BlONGYEneFEY0bQGU40ZgSd4URjRtAZTjRmBJ3hRGNG0BlONGYEneFEY0bQGU40ZgSd4URjRtAZTjRmBJ3hROP/D2kWRXKUH4DIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7FD29B3C3FD0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7FD29B3C36D8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看数据样例\n",
    "!sed -n \"5,5p\" ../datapath/datasets/MUGE/valid_texts.jsonl # 验证集第三条样本，对应上面第5行\n",
    "\n",
    "import lmdb\n",
    "import base64\n",
    "from io import BytesIO\n",
    "from PIL import Image\n",
    "\n",
    "image_ids = [225106, 139500, 349934, 941660, 515959] # 模型预测的top-5相关图片\n",
    "\n",
    "lmdb_imgs = \"../datapath/datasets/MUGE/lmdb/valid/imgs\"\n",
    "env_imgs = lmdb.open(lmdb_imgs, readonly=True, create=False, lock=False, readahead=False, meminit=False)\n",
    "txn_imgs = env_imgs.begin(buffers=True)\n",
    "for image_id in image_ids:\n",
    "    image_b64 = txn_imgs.get(\"{}\".format(image_id).encode('utf-8')).tobytes()\n",
    "    img = Image.open(BytesIO(base64.urlsafe_b64decode(image_b64)))\n",
    "    img.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Recall计算\n",
    "\n",
    "我们提供了评测脚本计算检索任务的Recall@1/5/10，同时给出mean recall（Recall@1/5/10的平均数）。运行如下命令即可获取分数:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2023-01-14T13:32:42.857040Z",
     "iopub.status.busy": "2023-01-14T13:32:42.856665Z",
     "iopub.status.idle": "2023-01-14T13:32:43.610919Z",
     "shell.execute_reply": "2023-01-14T13:32:43.609924Z",
     "shell.execute_reply.started": "2023-01-14T13:32:42.857004Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\n",
      "python cn_clip/eval/evaluation.py     ../datapath//datasets/MUGE/valid_texts.jsonl     ../datapath//datasets/MUGE/valid_predictions.jsonl     output.json;\n",
      "cat output.json\n",
      "\n",
      "Read standard from ../datapath//datasets/MUGE/valid_texts.jsonl\n",
      "Read user submit file from ../datapath//datasets/MUGE/valid_predictions.jsonl\n",
      "The evaluation finished successfully.\n",
      "{\"success\": true, \"score\": 75.33280085197018, \"scoreJson\": {\"score\": 75.33280085197018, \"mean_recall\": 75.33280085197018, \"r1\": 56.86900958466453, \"r5\": 81.21006389776358, \"r10\": 87.91932907348243}}"
     ]
    }
   ],
   "source": [
    "# 根据top-10预测结果，计算验证集的Recall@1/5/10，同时给出mean recall（Recall@1/5/10的平均数）\n",
    "\n",
    "split=\"valid\" # 指定计算valid或test集特征\n",
    "\n",
    "run_command = \"export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\" + \\\n",
    "f\"\"\"\n",
    "python cn_clip/eval/evaluation.py \\\n",
    "    {DATAPATH}/datasets/{dataset_name}/{split}_texts.jsonl \\\n",
    "    {DATAPATH}/datasets/{dataset_name}/{split}_predictions.jsonl \\\n",
    "    output.json;\n",
    "cat output.json\n",
    "\"\"\"\n",
    "print(run_command.lstrip())\n",
    "!{run_command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到验证集上，我们取得了~75的Mean Recall值，同时也给出了详细的Recall@1/5/10. 这个分数，已经比直接使用同规模预训练ckpt进行预测（zero-shot）的71.1更高，说明我们的finetune让模型更加适配到电商检索任务的领域数据上。通过调节不同的超参，上面finetune的分数还有很大的提升空间。在我们的实验中，Chinese-CLIP各规模zero-shot以及finetune，在验证集上可以得到的分数大体如下（更多Chinese-CLIP在其他任务的实验结果，请详见Github代码库 https://github.com/OFA-Sys/Chinese-CLIP/blob/master/Results.md ）："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MUGE Text-to-Image Retrieval (Official Validation Set)**:\n",
    "<table border=\"1\" width=\"100%\">\n",
    "    <tr align=\"center\">\n",
    "        <th>Setup</th><th colspan=\"4\">Zero-shot</th><th colspan=\"4\">Finetune</th>\n",
    "    </tr>\n",
    "    <tr align=\"center\">\n",
    "        <td>Metric</td><td>R@1</td><td>R@5</td><td>R@10</td><td>MR</td><td>R@1</td><td>R@5</td><td>R@10</td><td>MR</td>\n",
    "    </tr>\n",
    "\t<tr align=\"center\">\n",
    "        <td>CN-CLIP<sub>RN50</sub></td><td>42.6</td><td>68.6</td><td>77.9</td><td>63.0</td><td>48.6</td><td>75.1</td><td>84.0</td><td>69.2</td>\n",
    "    </tr>  \n",
    "\t<tr align=\"center\">\n",
    "        <td>CN-CLIP<sub>ViT-B/16</sub></td><td>52.1</td><td>76.7</td><td>84.4</td><td>71.1</td><td>58.4</td><td>83.6</td><td>90.0</td><td>77.4</td>\n",
    "    </tr>\n",
    "\t<tr align=\"center\">\n",
    "        <td>CN-CLIP<sub>ViT-L/14</sub></td><td>56.3</td><td>79.8</td><td>86.2</td><td>74.1</td><td>63.3</td><td>85.6</td><td>91.3</td><td>80.1</td>\n",
    "    </tr>  \n",
    "\t<tr align=\"center\">\n",
    "        <td>CN-CLIP<sub>ViT-L/14@336px</sub></td><td>59.0</td><td>81.4</td><td>87.8</td><td>76.1</td><td>65.3</td><td>86.7</td><td>92.1</td><td>81.3</td>\n",
    "    </tr>    \n",
    "\t<tr align=\"center\">\n",
    "        <td>CN-CLIP<sub>ViT-H/14</sub></td><td><b>63.0</b></td><td><b>84.1</b></td><td><b>89.2</b></td><td><b>78.8</b></td><td><b>68.9</b></td><td><b>88.7</b></td><td><b>93.1</b></td><td><b>83.6</b></td>\n",
    "    </tr>    \n",
    "</table>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    }
   },
   "source": [
    "## 准备测试集结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们在测试集上，再运行一次和验证集同样的特征计算和KNN召回流程，只是数据换成测试集，从而准备一份测试集预测结果，用于提交官方"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-14T13:45:44.959643Z",
     "iopub.status.busy": "2023-01-14T13:45:44.959352Z",
     "iopub.status.idle": "2023-01-14T13:52:18.502675Z",
     "shell.execute_reply": "2023-01-14T13:52:18.501907Z",
     "shell.execute_reply.started": "2023-01-14T13:45:44.959618Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\n",
      "python -u cn_clip/eval/extract_features.py     --extract-image-feats     --extract-text-feats     --image-data=\"../datapath//datasets/MUGE/lmdb/test/imgs\"     --text-data=\"../datapath//datasets/MUGE/test_texts.jsonl\"     --img-batch-size=32     --text-batch-size=32     --context-length=52     --resume=../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt     --vision-model=ViT-B-16     --text-model=RoBERTa-wwm-ext-base-chinese\n",
      "\n",
      "/home/pai/lib/python3.6/site-packages/OpenSSL/crypto.py:12: CryptographyDeprecationWarning: Python 3.6 is no longer supported by the Python core team. Therefore, support for it is deprecated in cryptography and will be removed in a future release.\n",
      "  from cryptography import x509\n",
      "Params:\n",
      "  context_length: 52\n",
      "  debug: False\n",
      "  extract_image_feats: True\n",
      "  extract_text_feats: True\n",
      "  image_data: ../datapath//datasets/MUGE/lmdb/test/imgs\n",
      "  image_feat_output_path: None\n",
      "  img_batch_size: 32\n",
      "  precision: amp\n",
      "  resume: ../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt\n",
      "  text_batch_size: 32\n",
      "  text_data: ../datapath//datasets/MUGE/test_texts.jsonl\n",
      "  text_feat_output_path: None\n",
      "  text_model: RoBERTa-wwm-ext-base-chinese\n",
      "  vision_model: ViT-B-16\n",
      "Loading vision model config from cn_clip/clip/model_configs/ViT-B-16.json\n",
      "Loading text model config from cn_clip/clip/model_configs/RoBERTa-wwm-ext-base-chinese.json\n",
      "Preparing image inference dataset.\n",
      "Preparing text inference dataset.\n",
      "Begin to load model checkpoint from ../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt.\n",
      "=> loaded checkpoint '../datapath//experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt' (epoch 1 @ 5215 steps)\n",
      "Make inference for texts...\n",
      "100%|█████████████████████████████████████████| 157/157 [00:07<00:00, 21.72it/s]\n",
      "5004 text features are stored in ../datapath//datasets/MUGE/test_texts.txt_feat.jsonl\n",
      "Make inference for images...\n",
      "100%|█████████████████████████████████████████| 950/950 [06:13<00:00,  2.54it/s]\n",
      "30399 image features are stored in ../datapath//datasets/MUGE/test_imgs.img_feat.jsonl\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# 为测试集图片池和query文本计算特征\n",
    "dataset_name=\"MUGE\"\n",
    "split=\"test\" # 指定计算valid或test集特征\n",
    "resume=f\"{DATAPATH}/experiments/muge_finetune_vit-b-16_roberta-base_bs48_1gpu/checkpoints/epoch_latest.pt\"\n",
    "\n",
    "run_command = \"export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\" + \\\n",
    "f\"\"\"\n",
    "python -u cn_clip/eval/extract_features.py \\\n",
    "    --extract-image-feats \\\n",
    "    --extract-text-feats \\\n",
    "    --image-data=\"{DATAPATH}/datasets/{dataset_name}/lmdb/{split}/imgs\" \\\n",
    "    --text-data=\"{DATAPATH}/datasets/{dataset_name}/{split}_texts.jsonl\" \\\n",
    "    --img-batch-size=32 \\\n",
    "    --text-batch-size=32 \\\n",
    "    --context-length=52 \\\n",
    "    --resume={resume} \\\n",
    "    --vision-model=ViT-B-16 \\\n",
    "    --text-model=RoBERTa-wwm-ext-base-chinese\n",
    "\"\"\"\n",
    "print(run_command.lstrip())\n",
    "!{run_command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-14T13:53:09.035737Z",
     "iopub.status.busy": "2023-01-14T13:53:09.035458Z",
     "iopub.status.idle": "2023-01-14T13:56:00.198365Z",
     "shell.execute_reply": "2023-01-14T13:56:00.197573Z",
     "shell.execute_reply.started": "2023-01-14T13:53:09.035711Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\n",
      "python -u cn_clip/eval/make_topk_predictions.py     --image-feats=\"../datapath//datasets/MUGE/test_imgs.img_feat.jsonl\"     --text-feats=\"../datapath//datasets/MUGE/test_texts.txt_feat.jsonl\"     --top-k=10     --eval-batch-size=32768     --output=\"../datapath//datasets/MUGE/test_predictions.jsonl\"\n",
      "\n",
      "Params:\n",
      "  eval_batch_size: 32768\n",
      "  image_feats: ../datapath//datasets/MUGE/test_imgs.img_feat.jsonl\n",
      "  output: ../datapath//datasets/MUGE/test_predictions.jsonl\n",
      "  text_feats: ../datapath//datasets/MUGE/test_texts.txt_feat.jsonl\n",
      "  top_k: 10\n",
      "Begin to load image features...\n",
      "30399it [00:07, 4257.35it/s]\n",
      "Finished loading image features.\n",
      "Begin to compute top-10 predictions for texts...\n",
      "5004it [02:40, 31.19it/s]\n",
      "Top-10 predictions are saved in ../datapath//datasets/MUGE/test_predictions.jsonl\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# 进行KNN检索，为测试集每个query，匹配特征余弦相似度最高的top-10商品图片\n",
    "\n",
    "split=\"test\" # 指定计算valid或test集特征\n",
    "\n",
    "run_command = \"export PYTHONPATH=${PYTHONPATH}:`pwd`/cn_clip;\" + \\\n",
    "f\"\"\"\n",
    "python -u cn_clip/eval/make_topk_predictions.py \\\n",
    "    --image-feats=\"{DATAPATH}/datasets/{dataset_name}/{split}_imgs.img_feat.jsonl\" \\\n",
    "    --text-feats=\"{DATAPATH}/datasets/{dataset_name}/{split}_texts.txt_feat.jsonl\" \\\n",
    "    --top-k=10 \\\n",
    "    --eval-batch-size=32768 \\\n",
    "    --output=\"{DATAPATH}/datasets/{dataset_name}/{split}_predictions.jsonl\"\n",
    "\"\"\"\n",
    "print(run_command.lstrip())\n",
    "!{run_command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-14T13:56:02.000527Z",
     "iopub.status.busy": "2023-01-14T13:56:02.000133Z",
     "iopub.status.idle": "2023-01-14T13:56:02.302021Z",
     "shell.execute_reply": "2023-01-14T13:56:02.301051Z",
     "shell.execute_reply.started": "2023-01-14T13:56:02.000481Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"text_id\": 342160, \"image_ids\": [599057, 224560, 282239, 908966, 155774, 511583, 368883, 611578, 808493, 598944]}\n",
      "{\"text_id\": 342169, \"image_ids\": [686662, 191142, 344670, 1122644, 986797, 406083, 969455, 424464, 2971, 251105]}\n",
      "{\"text_id\": 342177, \"image_ids\": [75011, 173661, 108900, 454071, 814416, 1114918, 331928, 474673, 98571, 1069979]}\n",
      "{\"text_id\": 342198, \"image_ids\": [526032, 277415, 1015004, 443782, 612288, 862027, 944337, 780085, 1122730, 442797]}\n",
      "{\"text_id\": 342202, \"image_ids\": [258830, 270450, 426942, 807698, 529722, 465381, 777309, 937790, 650457, 23176]}\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 ../datapath/datasets/MUGE/test_predictions.jsonl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该结果下载并提交到天池后，预期将取得56.5/80.6/87.5左右的Recall@1/5/10，即74.9左右的Mean Recall，可能由于finetune的随机性有一些小幅度的浮动"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上就是我们使用Chinese-CLIP，完成电商图文检索挑战赛的一个完整的baseline流程，欢迎大家在此基础上进一步提升效果。也希望大家多多支持Chinese-CLIP，如果觉得有帮助请为我们的 **[Github repo](https://github.com/OFA-Sys/Chinese-CLIP)** 点点star⭐️，多提宝贵的意见！"
   ]
  }
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